Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method includes: transforming pieces of attribute information of three-dimensional points included in point cloud data into coefficient values; and encoding the coefficient values to generate a bitstream. In the transforming, weighting calculation is performed hierarchically to generate the coefficient values belonging to one of layers, the weighting calculation separating each of the pieces of attribute information into a high-frequency component and a low-frequency component. In the weighting calculation, the weighting calculation is performed using weights fixed or not fixed in the layers. The bitstream includes first information indicating whether to fix the weights in the layers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2020/016970 filed on Apr. 17, 2020,claiming the benefit of priority of U.S. Provisional Patent ApplicationNo. 62/836,335 filed on Apr. 19, 2019 and U.S. Provisional PatentApplication No. 62/837,427 filed on Apr. 23, 2019, the entire contentsof which are hereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point group in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point group are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point groupnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include MPEG-4 AVC and HEVCstandardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle is known (for example, seeInternational Publication WO 2014/020663).

SUMMARY

There has been a demand for improving accuracy in a three-dimensionaldata encoding process.

The present disclosure has an object to provide a three-dimensional dataencoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of improving accuracy.

A three-dimensional data encoding method according to one aspect of thepresent disclosure includes: transforming pieces of attributeinformation of three-dimensional points included in point cloud datainto coefficient values; and encoding the coefficient values to generatea bitstream. In the transforming, weighting calculation is performedhierarchically to generate the coefficient values belonging to one oflayers, the weighting calculation separating each of the pieces ofattribute information into a high-frequency component and alow-frequency component. In the weighting calculation, the weightingcalculation is performed using weights fixed or not fixed in the layers.The bitstream includes first information indicating whether to fix theweights in the layers.

A three-dimensional data decoding method according to one aspect of thepresent disclosure includes: obtaining, from a bitstream, firstinformation indicating whether to fix weights in layers; decodingcoefficient values from the bitstream; and inverse transforming thecoefficient values to generate pieces of attribute information ofthree-dimensional points included in point cloud data. The coefficientvalues belong to one of the layers. In the inverse transforming, inverseweighting calculation is performed to generate the pieces of attributeinformation, the inverse weighting calculation combining the coefficientvalues with a high-frequency component and a low-frequency component. Inthe inverse weighting calculation, the inverse weighting calculation isperformed using the weights fixed or not fixed in the layers, accordingto the first information.

The present disclosure provides a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of improving accuracy.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram showing the structure of encoded three-dimensionaldata according to Embodiment 1;

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS according to Embodiment1;

FIG. 3 is a diagram showing an example of prediction structures amonglayers according to Embodiment 1;

FIG. 4 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 5 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 6 is a diagram showing an example of meta information according toEmbodiment 1;

FIG. 7 is a schematic diagram showing three-dimensional data beingtransmitted/received between vehicles according to Embodiment 2;

FIG. 8 is a diagram showing an example of three-dimensional datatransmitted between vehicles according to Embodiment 2;

FIG. 9 is a diagram that illustrates processes of transmittingthree-dimensional data according to Embodiment 3;

FIG. 10 is a diagram showing a structure of a system according toEmbodiment 4;

FIG. 11 is a block diagram of a client device according to Embodiment 4;

FIG. 12 is a block diagram of a server according to Embodiment 4;

FIG. 13 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 4;

FIG. 14 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 4;

FIG. 15 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 4;

FIG. 16 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 4;

FIG. 17 is a diagram showing a structure of a variation of the systemaccording to Embodiment 4;

FIG. 18 is a diagram showing a structure of the server and clientdevices according to Embodiment 4;

FIG. 19 is a diagram illustrating a configuration of a server and aclient device according to Embodiment 5;

FIG. 20 is a flowchart of a process performed by the client deviceaccording to Embodiment 5;

FIG. 21 is a diagram illustrating a configuration of a sensorinformation collection system according to Embodiment 5;

FIG. 22 is a diagram showing an example of a volume according toEmbodiment 6;

FIG. 23 is a diagram showing an example of an octree representation ofthe volume according to Embodiment 6;

FIG. 24 is a diagram showing an example of bit sequences of the volumeaccording to Embodiment 6;

FIG. 25 is a diagram showing an example of an octree representation of avolume according to Embodiment 6;

FIG. 26 is a diagram showing an example of the volume according toEmbodiment 6;

FIG. 27 is a diagram for describing the encoding of the attributeinformation by using a RAHT according to Embodiment 7;

FIG. 28 is a diagram showing an example of setting a quantization scalefor each hierarchy according to Embodiment 7;

FIG. 29 is a diagram showing an example of a first code sequence and asecond code sequence according to Embodiment 7;

FIG. 30 is a diagram showing an example of a truncated unary codeaccording to Embodiment 7;

FIG. 31 is a diagram for describing the inverse Haar conversionaccording to Embodiment 7;

FIG. 32 is a diagram showing a syntax example of the attributeinformation according to Embodiment 7;

FIG. 33 is a diagram showing an example of a coding coefficient andZeroCnt according to Embodiment 7;

FIG. 34 is a flowchart of the three-dimensional data encoding processingaccording to Embodiment 7;

FIG. 35 is a flowchart of the attribute information encoding processingaccording to Embodiment 7;

FIG. 36 is a flowchart of the coding coefficient encoding processingaccording to Embodiment 7;

FIG. 37 is a flowchart of the three-dimensional data decoding processingaccording to Embodiment 7;

FIG. 38 is a flowchart of the attribute information decoding processingaccording to Embodiment 7;

FIG. 39 is a flowchart the coding coefficient decoding processingaccording to Embodiment 7;

FIG. 40 is a block diagram of an attribute information encoder accordingto Embodiment 7;

FIG. 41 is a block diagram of an attribute information decoder accordingto Embodiment 7;

FIG. 42 is a diagram showing an example of a first code sequence and asecond code sequence according to a modification of Embodiment 7;

FIG. 43 is a diagram showing a syntax example of the attributeinformation according to the modification of Embodiment 7;

FIG. 44 is a diagram showing an example of a coding coefficient,ZeroCnt, and TotalZeroCnt according to the modification of Embodiment 7;

FIG. 45 is a flowchart of the coding coefficient encoding processingaccording to the modification of Embodiment 7;

FIG. 46 is a flowchart of the coding coefficient decoding processingaccording to the modification of Embodiment 7;

FIG. 47 is a diagram showing a syntax example of the attributeinformation according to the modification of Embodiment 7;

FIG. 48 is a block diagram showing a configuration of athree-dimensional data encoding device according to Embodiment 8;

FIG. 49 is a block diagram showing a configuration of athree-dimensional data decoding device according to Embodiment 8;

FIG. 50 is a diagram showing an example of the setting of LoDs accordingto Embodiment 8;

FIG. 51 is a diagram showing an example of a hierarchical structure ofRAHT according to Embodiment 8;

FIG. 52 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 8;

FIG. 53 is a block diagram of a divider according to Embodiment 8;

FIG. 54 is a block diagram of an attribute information encoder accordingto Embodiment 8;

FIG. 55 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 8;

FIG. 56 is a block diagram of a attribute information decoder accordingto Embodiment 8;

FIG. 57 is a diagram showing an example of the setting of a quantizationparameter in the tile division and the slice division according toEmbodiment 8;

FIG. 58 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 8;

FIG. 59 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 8;

FIG. 60 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 61 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 62 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 8;

FIG. 63 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 64 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 65 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 66 is a flowchart of an attribute information encoding processaccording to Embodiment 8;

FIG. 67 is a flowchart of a ΔQP determination process according toEmbodiment 8;

FIG. 68 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 69 is a flowchart of an attribute information decoding processaccording to Embodiment 8;

FIG. 70 is a block diagram of an attribute information encoder accordingto Embodiment 8;

FIG. 71 is a block diagram of an attribute information decoder accordingto Embodiment 8;

FIG. 72 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 8;

FIG. 73 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 74 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 75 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 76 is a flowchart of an attribute information encoding processaccording to Embodiment 8;

FIG. 77 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 78 is a flowchart of an attribute information decoding processaccording to Embodiment 8;

FIG. 79 is a block diagram of an attribute information encoder accordingto Embodiment 8;

FIG. 80 is a block diagram of an attribute information decoder accordingto Embodiment 8;

FIG. 81 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 8;

FIG. 82 is a diagram for illustrating a schematic configuration of anattribute information encoder according to Embodiment 9;

FIG. 83 is a diagram for illustrating a schematic configuration of anattribute information decoder according to Embodiment 9;

FIG. 84 is a diagram for illustrating a schematic configuration of anattribute information encoder according to a variation of Embodiment 9;

FIG. 85 is a diagram for illustrating a schematic configuration of anattribute information decoder according to a variation of Embodiment 9;

FIG. 86 is a diagram for illustrating a re-ordering process according toEmbodiment 9;

FIG. 87 is a diagram for illustrating a first example of atransformation process for attribute information according to Embodiment9;

FIG. 88 is a diagram for illustrating a second example of thetransformation process for attribute information according to Embodiment9;

FIG. 89 is a diagram for illustrating a third example of thetransformation process for attribute information according to Embodiment9;

FIG. 90 is a diagram for illustrating a fourth example of thetransformation process for attribute information according to Embodiment9;

FIG. 91 is a diagram for illustrating a fifth example of thetransformation process for attribute information according to Embodiment9;

FIG. 92 is a diagram for illustrating examples of a connection betweenvoxels and normal vectors in the examples according to Embodiment 9;

FIG. 93 is a diagram illustrating an example of geometry informationaccording to Embodiment 9;

FIG. 94 is a diagram illustrating an example of selecting a coding tableusing geometry information according to Embodiment 9;

FIG. 95 is a diagram for illustrating a sixth example of thetransformation process for attribute information according to Embodiment9;

FIG. 96 is a diagram for illustrating a seventh example of thetransformation process for attribute information according to Embodiment9;

FIG. 97 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 9;

FIG. 98 is a block diagram of an attribute information encoder accordingto Embodiment 9;

FIG. 99 is a block diagram of a point cloud re-ordering unit accordingto Embodiment 9;

FIG. 100 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 9;

FIG. 101 is a block diagram of an attribute information decoderaccording to Embodiment 9;

FIG. 102 is a block diagram of a three-dimensional data encoding deviceaccording to a variation of Embodiment 9;

FIG. 103 is a block diagram of an attribute information encoderaccording to a variation of Embodiment 9;

FIG. 104 is a block diagram of a three-dimensional data decoding deviceaccording to a variation of Embodiment 9;

FIG. 105 is a block diagram of an attribute information decoderaccording to a variation of Embodiment 9;

FIG. 106 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 9;

FIG. 107 is a flowchart of an attribute information encoding processaccording to Embodiment 9;

FIG. 108 is a flowchart of an attribute information re-ordering processaccording to Embodiment 9;

FIG. 109 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 9;

FIG. 110 is a flowchart of an attribute information decoding processaccording to Embodiment 9;

FIG. 111 is a flowchart of an attribute information re-ordering processaccording to Embodiment 9;

FIG. 112 is a flowchart of a three-dimensional data encoding methodaccording to Embodiment 10;

FIG. 113 is a diagram for illustrating encoding of attribute informationusing RAHT according to Embodiment 10;

FIG. 114 is a flowchart of a three-dimensional data decoding methodaccording to Embodiment 10;

FIG. 115 is a diagram for illustrating decoding of attribute informationusing RAHT according to Embodiment 10;

FIG. 116 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 10;

FIG. 117 is a flowchart of an attribute information encoding processaccording to Embodiment 10;

FIG. 118 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 10;

FIG. 119 is a flowchart of an attribute information decoding processaccording to Embodiment 10;

FIG. 120 is a block diagram of attribute information encoder included ina three-dimensional data encoding device according to Embodiment 10;

FIG. 121 is a block diagram of attribute information decoder included ina three-dimensional data decoding device according to Embodiment 10;

FIG. 122 is a diagram showing a configuration of a three-dimensionaldata encoding device according to Embodiment 11;

FIG. 123 is a diagram showing a configuration of a three-dimensionaldata decoding device according to Embodiment 11;

FIG. 124 is a diagram for illustrating RAHT according to Embodiment 11;

FIG. 125 is a diagram for illustrating an integer-to-integer transformaccording to Embodiment 11;

FIG. 126 is a diagram for illustrating a hierarchical transformprocessing according to Embodiment 11;

FIG. 127 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 11;

FIG. 128 is a block diagram of a lossless attribute information encoderaccording to Embodiment 11;

FIG. 129 is a block diagram of an integer transformer according toEmbodiment 11;

FIG. 130 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 11;

FIG. 131 is a block diagram of a lossless attribute information decoderaccording to Embodiment 11;

FIG. 132 is a block diagram of an inverse integer transformer accordingto Embodiment 11;

FIG. 133 is a flowchart of a lossless attribute information encodingprocessing according to Embodiment 11;

FIG. 134 is a flowchart of a lossless attribute information decodingprocessing according to Embodiment 11;

FIG. 135 is a diagram showing an example configuration of an integerHaar transformer according to Embodiment 11;

FIG. 136 is a diagram showing an example configuration of an inverseinteger Haar transformer according to Embodiment 11;

FIG. 137 is a diagram showing a configuration of a three-dimensionaldata encoding device according to Embodiment 11;

FIG. 138 is a diagram showing a configuration of a three-dimensionaldata decoding device according to Embodiment 11;

FIG. 139 is a diagram showing a configuration of a three-dimensionaldata encoding device according to Embodiment 11;

FIG. 140 is a diagram showing a configuration of a three-dimensionaldata decoding device according to Embodiment 11;

FIG. 141 is a diagram showing an example configuration of a bitstreamaccording to Embodiment 11;

FIG. 142 is a diagram showing an example configuration of a bitstreamaccording to Embodiment 11;

FIG. 143 is a diagram showing a configuration of a three-dimensionaldata encoding device according to Embodiment 12;

FIG. 144 is a diagram showing a configuration of a three-dimensionaldata decoding device according to Embodiment 12;

FIG. 145 is a diagram showing an example configuration of a bitstreamaccording to Embodiment 12;

FIG. 146 is a diagram showing an example configuration of an integerRAHT/Haar transformer according to Embodiment 12;

FIG. 147 is a diagram showing an example configuration of an inverseinteger RAHT/Haar transformer according to Embodiment 12;

FIG. 148 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 12; and

FIG. 149 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 12.

DETAILED DESCRIPTION OF THE EMBODIMENTS

A three-dimensional data encoding method according to one aspect of thepresent disclosure includes: transforming pieces of attributeinformation of three-dimensional points included in point cloud datainto coefficient values; and encoding the coefficient values to generatea bitstream. In the transforming, weighting calculation is performedhierarchically to generate the coefficient values belonging to one oflayers, the weighting calculation separating each of the pieces ofattribute information into a high-frequency component and alow-frequency component. In the weighting calculation, the weightingcalculation is performed using weights fixed or not fixed in the layers.The bitstream includes first information indicating whether to fix theweights in the layers.

According to the three-dimensional data encoding method, the weight isfixed for a plurality of layers, so that the loss caused by thetransform can be reduced, and the accuracy can be improved.

For example, when the weights are fixed in the layers, the weights maybe fixed to 1.

For example, in the weighting calculation: first attribute informationmay be subtracted from second attribute information to calculate a firstvalue, the first attribute information and the second attributeinformation being included in the pieces of attribute information; andthe first value may be divided by a first coefficient to calculate thehigh-frequency component. The first coefficient may depend on aquantization step and the weights.

For example, in the weighting calculation: the first value may bemultiplied by a second coefficient depending on the weights to calculatea second value; the second value may be shifted down by a predeterminedbit count and shifted up by the predetermined bit count to calculate athird value; and the third value may be added to the first attributeinformation to calculate the low-frequency component.

A three-dimensional data decoding method according to one aspect of thepresent disclosure includes: obtaining, from a bitstream, firstinformation indicating whether to fix weights in layers; decodingcoefficient values from the bitstream; and inverse transforming thecoefficient values to generate pieces of attribute information ofthree-dimensional points included in point cloud data. The coefficientvalues belong to one of the layers. In the inverse transforming, inverseweighting calculation is performed to generate the pieces of attributeinformation, the inverse weighting calculation combining the coefficientvalues with a high-frequency component and a low-frequency component. Inthe inverse weighting calculation, the inverse weighting calculation isperformed using the weights fixed or not fixed in the layers, accordingto the first information.

According to the three-dimensional data decoding method, the weight isfixed for a plurality of layers, so that the loss caused by thetransform can be reduced, and the accuracy can be improved.

For example, when the weights are fixed in the layers, the weights maybe fixed to 1.

For example, in the inverse weighting calculation: the high-frequencycomponent may be multiplied by a first coefficient to calculate a firstvalue; first attribute information included in the pieces of attributeinformation may be calculated from a second value based on thelow-frequency component; and the second value may be subtracted from thefirst value to calculate second attribute information included in thepieces of attribute information. The first coefficient may depend on aquantization step and the weights.

For example, in the inverse weighting calculation: the first value maybe multiplied by a second coefficient depending on the weights tocalculate a third value; the third value may be shifted down by apredetermined bit count and shifted up by the predetermined bit count tocalculate a fourth value; and the low-frequency component is subtractedfrom the fourth value to calculate the second value.

A three-dimensional data encoding device according to one aspect of thepresent disclosure includes a processor and memory. Using the memory,the processor: transforms pieces of attribute information ofthree-dimensional points included in point cloud data into coefficientvalues; and encodes the coefficient values to generate a bitstream. Inthe transforming, weighting calculation is performed hierarchically togenerate the coefficient values belonging to one of layers, theweighting calculation separating each of the pieces of attributeinformation into a high-frequency component and a low-frequencycomponent. In the weighting calculation, the weighting calculation isperformed using weights fixed or not fixed in the layers. The bitstreamincludes first information indicating whether to fix the weights in thelayers.

With this, since the three-dimensional data encoding device is capableof reducing the loss caused by the transform, by fixing the weight for aplurality of layers, the three-dimensional data encoding device iscapable of improving the accuracy.

A three-dimensional data decoding device according to one aspect of thepresent disclosure includes a processor and memory. Using the memory,the processor: obtains, from a bitstream, first information indicatingwhether to fix weights in layers; decodes coefficient values from thebitstream; and inverse transforms the coefficient values to generatepieces of attribute information of three-dimensional points included inpoint cloud data. The coefficient values belong to one of the layers. Inthe inverse transforming, inverse weighting calculation is performed togenerate the pieces of attribute information, the inverse weightingcalculation combining the coefficient values with a high-frequencycomponent and a low-frequency component. In the inverse weightingcalculation, the inverse weighting calculation is performed using theweights fixed or not fixed in the layers, according to the firstinformation.

With this, since the three-dimensional data decoding device is capableof reducing the loss caused by the transform, by fixing the weight for aplurality of layers, the three-dimensional data decoding device iscapable of improving the accuracy.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a CD-ROM, ormay be implemented as any combination of a system, a method, anintegrated circuit, a computer program, and a recording medium.

The following describes embodiments with reference to the drawings. Itis to be noted that the following embodiments indicate exemplaryembodiments of the present disclosure. The numerical values, shapes,materials, constituent elements, the arrangement and connection of theconstituent elements, steps, the processing order of the steps, etc.indicated in the following embodiments are mere examples, and thus arenot intended to limit the present disclosure. Of the constituentelements described in the following embodiments, constituent elementsnot recited in any one of the independent claims that indicate thebroadest concepts will be described as optional constituent elements.

Embodiment 1

First, the data structure of encoded three-dimensional data (hereinafteralso referred to as encoded data) according to the present embodimentwill be described. FIG. 1 is a diagram showing the structure of encodedthree-dimensional data according to the present embodiment. FIG. 1 is adiagram showing the structure of encoded three-dimensional dataaccording to the present embodiment.

In the present embodiment, a three-dimensional space is divided intospaces (SPCs), which correspond to pictures in moving picture encoding,and the three-dimensional data is encoded on a SPC-by-SPC basis. EachSPC is further divided into volumes (VLMs), which correspond tomacroblocks, etc. in moving picture encoding, and predictions andtransforms are performed on a VLM-by-VLM basis. Each volume includes aplurality of voxels (VXLs), each being a minimum unit in which positioncoordinates are associated. Note that prediction is a process ofgenerating predictive three-dimensional data analogous to a currentprocessing unit by referring to another processing unit, and encoding adifferential between the predictive three-dimensional data and thecurrent processing unit, as in the case of predictions performed ontwo-dimensional images. Such prediction includes not only spatialprediction in which another prediction unit corresponding to the sametime is referred to, but also temporal prediction in which a predictionunit corresponding to a different time is referred to.

When encoding a three-dimensional space represented by point group datasuch as a point cloud, for example, the three-dimensional data encodingdevice (hereinafter also referred to as the encoding device) encodes thepoints in the point group or points included in the respective voxels ina collective manner, in accordance with a voxel size. Finer voxelsenable a highly-precise representation of the three-dimensional shape ofa point group, while larger voxels enable a rough representation of thethree-dimensional shape of a point group.

Note that the following describes the case where three-dimensional datais a point cloud, but three-dimensional data is not limited to a pointcloud, and thus three-dimensional data of any format may be employed.

Also note that voxels with a hierarchical structure may be used. In sucha case, when the hierarchy includes n levels, whether a sampling pointis included in the n−1th level or its lower levels (the lower levels ofthe n-th level) may be sequentially indicated. For example, when onlythe n-th level is decoded, and the n−1th level or its lower levelsinclude a sampling point, the n-th level can be decoded on theassumption that a sampling point is included at the center of a voxel inthe n-th level.

Also, the encoding device obtains point group data, using, for example,a distance sensor, a stereo camera, a monocular camera, a gyroscopesensor, or an inertial sensor.

As in the case of moving picture encoding, each SPC is classified intoone of at least the three prediction structures that include: intra SPC(I-SPC), which is individually decodable; predictive SPC (P-SPC) capableof only a unidirectional reference; and bidirectional SPC (B-SPC)capable of bidirectional references. Each SPC includes two types of timeinformation: decoding time and display time.

Furthermore, as shown in FIG. 1, a processing unit that includes aplurality of SPCs is a group of spaces (GOS), which is a random accessunit. Also, a processing unit that includes a plurality of GOSs is aworld (WLD).

The spatial region occupied by each world is associated with an absoluteposition on earth, by use of, for example, GPS, or latitude andlongitude information. Such position information is stored asmeta-information. Note that meta-information may be included in encodeddata, or may be transmitted separately from the encoded data.

Also, inside a GOS, all SPCs may be three-dimensionally adjacent to oneanother, or there may be a SPC that is not three-dimensionally adjacentto another SPC.

Note that the following also describes processes such as encoding,decoding, and reference to be performed on three-dimensional dataincluded in processing units such as GOS, SPC, and VLM, simply asperforming encoding/to encode, decoding/to decode, referring to, etc. ona processing unit. Also note that three-dimensional data included in aprocessing unit includes, for example, at least one pair of a spatialposition such as three-dimensional coordinates and an attribute valuesuch as color information.

Next, the prediction structures among SPCs in a GOS will be described. Aplurality of SPCs in the same GOS or a plurality of VLMs in the same SPCoccupy mutually different spaces, while having the same time information(the decoding time and the display time).

A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSscome in two types: closed GOS and open GOS. A closed GOS is a GOS inwhich all SPCs in the GOS are decodable when decoding starts from thefirst I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS isreferred to in one or more SPCs preceding the first I-SPC in the GOS inthe display time, and thus cannot be singly decoded.

Note that in the case of encoded data of map information, for example, aWLD is sometimes decoded in the backward direction, which is opposite tothe encoding order, and thus backward reproduction is difficult whenGOSs are interdependent. In such a case, a closed GOS is basically used.

Each GOS has a layer structure in height direction, and SPCs aresequentially encoded or decoded from SPCs in the bottom layer.

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS. FIG. 3 is a diagramshowing an example of prediction structures among layers.

A GOS includes at least one I-SPC. Of the objects in a three-dimensionalspace, such as a person, an animal, a car, a bicycle, a signal, and abuilding serving as a landmark, a small-sized object is especiallyeffective when encoded as an I-SPC. When decoding a GOS at a lowthroughput or at a high speed, for example, the three-dimensional datadecoding device (hereinafter also referred to as the decoding device)decodes only I-SPC(s) in the GOS.

The encoding device may also change the encoding interval or theappearance frequency of I-SPCs, depending on the degree of sparsenessand denseness of the objects in a WLD.

In the structure shown in FIG. 3, the encoding device or the decodingdevice encodes or decodes a plurality of layers sequentially from thebottom layer (layer 1). This increases the priority of data on theground and its vicinity, which involve a larger amount of information,when, for example, a self-driving car is concerned.

Regarding encoded data used for a drone, for example, encoding ordecoding may be performed sequentially from SPCs in the top layer in aGOS in height direction.

The encoding device or the decoding device may also encode or decode aplurality of layers in a manner that the decoding device can have arough grasp of a GOS first, and then the resolution is graduallyincreased. The encoding device or the decoding device may performencoding or decoding in the order of layers 3, 8, 1, 9 . . . , forexample.

Next, the handling of static objects and dynamic objects will bedescribed.

A three-dimensional space includes scenes or still objects such as abuilding and a road (hereinafter collectively referred to as staticobjects), and objects with motion such as a car and a person(hereinafter collectively referred to as dynamic objects). Objectdetection is separately performed by, for example, extracting keypointsfrom point cloud data, or from video of a camera such as a stereocamera. In this description, an example method of encoding a dynamicobject will be described.

A first method is a method in which a static object and a dynamic objectare encoded without distinction. A second method is a method in which adistinction is made between a static object and a dynamic object on thebasis of identification information.

For example, a GOS is used as an identification unit. In such a case, adistinction is made between a GOS that includes SPCs constituting astatic object and a GOS that includes SPCs constituting a dynamicobject, on the basis of identification information stored in the encodeddata or stored separately from the encoded data.

Alternatively, a SPC may be used as an identification unit. In such acase, a distinction is made between a SPC that includes VLMsconstituting a static object and a SPC that includes VLMs constituting adynamic object, on the basis of the identification information thusdescribed.

Alternatively, a VLM or a VXL may be used as an identification unit. Insuch a case, a distinction is made between a VLM or a VXL that includesa static object and a VLM or a VXL that includes a dynamic object, onthe basis of the identification information thus described.

The encoding device may also encode a dynamic object as at least one VLMor SPC, and may encode a VLM or a SPC including a static object and aSPC including a dynamic object as mutually different GOSs. When the GOSsize is variable depending on the size of a dynamic object, the encodingdevice separately stores the GOS size as meta-information.

The encoding device may also encode a static object and a dynamic objectseparately from each other, and may superimpose the dynamic object ontoa world constituted by static objects. In such a case, the dynamicobject is constituted by at least one SPC, and each SPC is associatedwith at least one SPC constituting the static object onto which the eachSPC is to be superimposed. Note that a dynamic object may be representednot by SPC(s) but by at least one VLM or VXL.

The encoding device may also encode a static object and a dynamic objectas mutually different streams.

The encoding device may also generate a GOS that includes at least oneSPC constituting a dynamic object. The encoding device may further setthe size of a GOS including a dynamic object (GOS_M) and the size of aGOS including a static object corresponding to the spatial region ofGOS_M at the same size (such that the same spatial region is occupied).This enables superimposition to be performed on a GOS-by-GOS basis.

SPC(s) included in another encoded GOS may be referred to in a P-SPC ora B-SPC constituting a dynamic object. In the case where the position ofa dynamic object temporally changes, and the same dynamic object isencoded as an object in a GOS corresponding to a different time,referring to SPC(s) across GOSs is effective in terms of compressionrate.

The first method and the second method may be selected in accordancewith the intended use of encoded data. When encoded three-dimensionaldata is used as a map, for example, a dynamic object is desired to beseparated, and thus the encoding device uses the second method.Meanwhile, the encoding device uses the first method when the separationof a dynamic object is not required such as in the case wherethree-dimensional data of an event such as a concert and a sports eventis encoded.

The decoding time and the display time of a GOS or a SPC are storable inencoded data or as meta-information. All static objects may have thesame time information. In such a case, the decoding device may determinethe actual decoding time and display time. Alternatively, a differentvalue may be assigned to each GOS or SPC as the decoding time, and thesame value may be assigned as the display time. Furthermore, as in thecase of the decoder model in moving picture encoding such asHypothetical Reference Decoder (HRD) compliant with HEVC, a model may beemployed that ensures that a decoder can perform decoding without failby having a buffer of a predetermined size and by reading a bitstream ata predetermined bit rate in accordance with the decoding times.

Next, the topology of GOSs in a world will be described. The coordinatesof the three-dimensional space in a world are represented by the threecoordinate axes (x axis, y axis, and z axis) that are orthogonal to oneanother. A predetermined rule set for the encoding order of GOSs enablesencoding to be performed such that spatially adjacent GOSs arecontiguous in the encoded data. In an example shown in FIG. 4, forexample, GOSs in the x and z planes are successively encoded. After thecompletion of encoding all GOSs in certain x and z planes, the value ofthe y axis is updated. Stated differently, the world expands in the yaxis direction as the encoding progresses. The GOS index numbers are setin accordance with the encoding order.

Here, the three-dimensional spaces in the respective worlds arepreviously associated one-to-one with absolute geographical coordinatessuch as GPS coordinates or latitude/longitude coordinates.Alternatively, each three-dimensional space may be represented as aposition relative to a previously set reference position. The directionsof the x axis, the y axis, and the z axis in the three-dimensional spaceare represented by directional vectors that are determined on the basisof the latitudes and the longitudes, etc. Such directional vectors arestored together with the encoded data as meta-information.

GOSs have a fixed size, and the encoding device stores such size asmeta-information. The GOS size may be changed depending on, for example,whether it is an urban area or not, or whether it is inside or outsideof a room. Stated differently, the GOS size may be changed in accordancewith the amount or the attributes of objects with information values.Alternatively, in the same world, the encoding device may adaptivelychange the GOS size or the interval between I-SPCs in GOSs in accordancewith the object density, etc. For example, the encoding device sets theGOS size to smaller and the interval between I-SPCs in GOSs to shorter,as the object density is higher.

In an example shown in FIG. 5, to enable random access with a finergranularity, a GOS with a high object density is partitioned into theregions of the third to tenth GOSs. Note that the seventh to tenth GOSsare located behind the third to sixth GOSs.

Embodiment 2

The present embodiment will describe a method of transmitting/receivingthree-dimensional data between vehicles.

FIG. 7 is a schematic diagram showing three-dimensional data 607 beingtransmitted/received between own vehicle 600 and nearby vehicle 601.

In three-dimensional data that is obtained by a sensor mounted on ownvehicle 600 (e.g., a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras),there appears a region, three-dimensional data of which cannot becreated, due to an obstacle such as nearby vehicle 601, despite thatsuch region is included in sensor detection range 602 of own vehicle 600(such region is hereinafter referred to as occlusion region 604). Also,while the obtainment of three-dimensional data of a larger space enablesa higher accuracy of autonomous operations, a range of sensor detectiononly by own vehicle 600 is limited.

Sensor detection range 602 of own vehicle 600 includes region 603,three-dimensional data of which is obtainable, and occlusion region 604.A range, three-dimensional data of which own vehicle 600 wishes toobtain, includes sensor detection range 602 of own vehicle 600 and otherregions. Sensor detection range 605 of nearby vehicle 601 includesocclusion region 604 and region 606 that is not included in sensordetection range 602 of own vehicle 600.

Nearby vehicle 601 transmits information detected by nearby vehicle 601to own vehicle 600. Own vehicle 600 obtains the information detected bynearby vehicle 601, such as a preceding vehicle, thereby obtainingthree-dimensional data 607 of occlusion region 604 and region 606outside of sensor detection range 602 of own vehicle 600. Own vehicle600 uses the information obtained by nearby vehicle 601 to complementthe three-dimensional data of occlusion region 604 and region 606outside of the sensor detection range.

The usage of three-dimensional data in autonomous operations of avehicle or a robot includes self-location estimation, detection ofsurrounding conditions, or both. For example, for self-locationestimation, three-dimensional data is used that is generated by ownvehicle 600 on the basis of sensor information of own vehicle 600. Fordetection of surrounding conditions, three-dimensional data obtainedfrom nearby vehicle 601 is also used in addition to thethree-dimensional data generated by own vehicle 600.

Nearby vehicle 601 that transmits three-dimensional data 607 to ownvehicle 600 may be determined in accordance with the state of ownvehicle 600. For example, the current nearby vehicle 601 is a precedingvehicle when own vehicle 600 is running straight ahead, an oncomingvehicle when own vehicle 600 is turning right, and a following vehiclewhen own vehicle 600 is rolling backward. Alternatively, the driver ofown vehicle 600 may directly specify nearby vehicle 601 that transmitsthree-dimensional data 607 to own vehicle 600.

Alternatively, own vehicle 600 may search for nearby vehicle 601 havingthree-dimensional data of a region that is included in a space,three-dimensional data of which own vehicle 600 wishes to obtain, andthat own vehicle 600 cannot obtain. The region own vehicle 600 cannotobtain is occlusion region 604, or region 606 outside of sensordetection range 602, etc.

Own vehicle 600 may identify occlusion region 604 on the basis of thesensor information of own vehicle 600. For example, own vehicle 600identifies, as occlusion region 604, a region which is included insensor detection range 602 of own vehicle 600, and three-dimensionaldata of which cannot be created.

The following describes example operations to be performed when avehicle that transmits three-dimensional data 607 is a precedingvehicle. FIG. 8 is a diagram showing an example of three-dimensionaldata to be transmitted in such case.

As FIG. 8 shows, three-dimensional data 607 transmitted from thepreceding vehicle is, for example, a sparse world (SWLD) of a pointcloud. Stated differently, the preceding vehicle createsthree-dimensional data (point cloud) of a WLD from information detectedby a sensor of such preceding vehicle, and extracts data having anamount of features greater than or equal to the threshold from suchthree-dimensional data of the WLD, thereby creating three-dimensionaldata (point cloud) of the SWLD. Subsequently, the preceding vehicletransmits the created three-dimensional data of the SWLD to own vehicle600.

Own vehicle 600 receives the SWLD, and merges the received SWLD with thepoint cloud created by own vehicle 600.

The SWLD to be transmitted includes information on the absolutecoordinates (the position of the SWLD in the coordinates system of athree-dimensional map). The merge is achieved by own vehicle 600overwriting the point cloud generated by own vehicle 600 on the basis ofsuch absolute coordinates.

The SWLD transmitted from nearby vehicle 601 may be: a SWLD of region606 that is outside of sensor detection range 602 of own vehicle 600 andwithin sensor detection range 605 of nearby vehicle 601; or a SWLD ofocclusion region 604 of own vehicle 600; or the SWLDs of the both. Ofthese SWLDs, a SWLD to be transmitted may also be a SWLD of a regionused by nearby vehicle 601 to detect the surrounding conditions.

Nearby vehicle 601 may change the density of a point cloud to transmit,in accordance with the communication available time, during which ownvehicle 600 and nearby vehicle 601 can communicate, and which is basedon the speed difference between these vehicles. For example, when thespeed difference is large and the communication available time is short,nearby vehicle 601 may extract three-dimensional points having a largeamount of features from the SWLD to decrease the density (data amount)of the point cloud.

The detection of the surrounding conditions refers to judging thepresence/absence of persons, vehicles, equipment for roadworks, etc.,identifying their types, and detecting their positions, travellingdirections, traveling speeds, etc.

Own vehicle 600 may obtain braking information of nearby vehicle 601instead of or in addition to three-dimensional data 607 generated bynearby vehicle 601. Here, the braking information of nearby vehicle 601is, for example, information indicating that the accelerator or thebrake of nearby vehicle 601 has been pressed, or the degree of suchpressing.

In the point clouds generated by the vehicles, the three-dimensionalspaces are segmented on a random access unit, in consideration oflow-latency communication between the vehicles. Meanwhile, in athree-dimensional map, etc., which is map data downloaded from theserver, a three-dimensional space is segmented in a larger random accessunit than in the case of inter-vehicle communication.

Data on a region that is likely to be an occlusion region, such as aregion in front of the preceding vehicle and a region behind thefollowing vehicle, is segmented on a finer random access unit aslow-latency data.

Data on a region in front of a vehicle has an increased importance whenon an expressway, and thus each vehicle creates a SWLD of a range with anarrowed viewing angle on a finer random access unit when running on anexpressway.

When the SWLD created by the preceding vehicle for transmission includesa region, the point cloud of which own vehicle 600 can obtain, thepreceding vehicle may remove the point cloud of such region to reducethe amount of data to transmit.

Embodiment 3

The present embodiment describes a method, etc. of transmittingthree-dimensional data to a following vehicle. FIG. 9 is a diagramshowing an exemplary space, three-dimensional data of which is to betransmitted to a following vehicle, etc.

Vehicle 801 transmits, at the time interval of Δt, three-dimensionaldata, such as a point cloud (a point group) included in a rectangularsolid space 802, having width W, height H, and depth D, located ahead ofvehicle 801 and distanced by distance L from vehicle 801, to acloud-based traffic monitoring system that monitors road situations or afollowing vehicle.

When a change has occurred in the three-dimensional data of a space thatis included in space 802 already transmitted in the past, due to avehicle or a person entering space 802 from outside, for example,vehicle 801 also transmits three-dimensional data of the space in whichsuch change has occurred.

Although FIG. 9 illustrates an example in which space 802 has arectangular solid shape, space 802 is not necessarily a rectangularsolid so long as space 802 includes a space on the forward road that ishidden from view of a following vehicle.

Distance L may be set to a distance that allows the following vehiclehaving received the three-dimensional data to stop safely. For example,set as distance L is the sum of; a distance traveled by the followingvehicle while receiving the three-dimensional data; a distance traveledby the following vehicle until the following vehicle starts speedreduction in accordance with the received data; and a distance requiredby the following vehicle to stop safely after starting speed reduction.These distances vary in accordance with the speed, and thus distance Lmay vary in accordance with speed V of the vehicle, just like L=a×V+b (aand b are constants).

Width W is set to a value that is at least greater than the width of thelane on which vehicle 801 is traveling. Width W may also be set to asize that includes an adjacent space such as right and left lanes and aside strip.

Depth D may have a fixed value, but may vary in accordance with speed Vof the vehicle, just like D=c×V+d (c and d are constants). Also, D thatis set to satisfy D>V×Δt enables the overlap of a space to betransmitted and a space transmitted in the past. This enables vehicle801 to transmit a space on the traveling road to the following vehicle,etc. completely and more reliably.

As described above, vehicle 801 transmits three-dimensional data of alimited space that is useful to the following vehicle, therebyeffectively reducing the amount of the three-dimensional data to betransmitted and achieving low-latency, low-cost communication.

Embodiment 4

In Embodiment 3, an example is described in which a client device of avehicle or the like transmits three-dimensional data to another vehicleor a server such as a cloud-based traffic monitoring system. In thepresent embodiment, a client device transmits sensor informationobtained through a sensor to a server or a client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 10 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LIDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 11 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g. transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 815 are a group of sensors, such as LIDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LIDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LIDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LIDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 12 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LIDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g. transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when the received sensor information1037 is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LIDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 13is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 14 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 15 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 16 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

The following describes variations of the present embodiment.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LIDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using the obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 17 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 18 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmits theobtained sensor information 1033 to server 901 or another mobile object.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1034 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another mobileobject 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using the received sensorinformation 1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses the received sensor information 1037,and creates three-dimensional data 1134 using sensor information 1132that has been decoded or decompressed. This enables server 901 to reducethe amount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

Embodiment 5

In the present embodiment, a variation of Embodiment 4 will bedescribed. FIG. 19 is a diagram illustrating a configuration of a systemaccording to the present embodiment. The system illustrated in FIG. 19includes server 2001, client device 2002A, and client device 2002B.

Client device 2002A and client device 2002B are each provided in amobile object such as a vehicle, and transmit sensor information toserver 2001. Server 2001 transmits a three-dimensional map (a pointcloud) to client device 2002A and client device 2002B.

Client device 2002A includes sensor information obtainer 2011, storage2012, and data transmission possibility determiner 2013. It should benoted that client device 2002B has the same configuration. Additionally,when client device 2002A and client device 2002B are not particularlydistinguished below, client device 2002A and client device 2002B arealso referred to as client device 2002.

FIG. 20 is a flowchart illustrating operation of client device 2002according to the present embodiment.

Sensor information obtainer 2011 obtains a variety of sensor informationusing sensors (a group of sensors) provided in a mobile object. In otherwords, sensor information obtainer 2011 obtains sensor informationobtained by the sensors (the group of sensors) provided in the mobileobject and indicating a surrounding state of the mobile object. Sensorinformation obtainer 2011 also stores the obtained sensor informationinto storage 2012. This sensor information includes at least one ofinformation obtained by LiDAR, a visible light image, an infrared image,or a depth image. Additionally, the sensor information may include atleast one of sensor position information, speed information, obtainmenttime information, or obtainment location information. Sensor positioninformation indicates a position of a sensor that has obtained sensorinformation. Speed information indicates a speed of the mobile objectwhen a sensor obtained sensor information. Obtainment time informationindicates a time when a sensor obtained sensor information. Obtainmentlocation information indicates a position of the mobile object or asensor when the sensor obtained sensor information.

Next, data transmission possibility determiner 2013 determines whetherthe mobile object (client device 2002) is in an environment in which themobile object can transmit sensor information to server 2001 (S2002).For example, data transmission possibility determiner 2013 may specify alocation and a time at which client device 2002 is present using GPSinformation etc., and may determine whether data can be transmitted.Additionally, data transmission possibility determiner 2013 maydetermine whether data can be transmitted, depending on whether it ispossible to connect to a specific access point.

When client device 2002 determines that the mobile object is in theenvironment in which the mobile object can transmit the sensorinformation to server 2001 (YES in S2002), client device 2002 transmitsthe sensor information to server 2001 (S2003). In other words, whenclient device 2002 becomes capable of transmitting sensor information toserver 2001, client device 2002 transmits the sensor information held byclient device 2002 to server 2001. For example, an access point thatenables high-speed communication using millimeter waves is provided inan intersection or the like. When client device 2002 enters theintersection, client device 2002 transmits the sensor information heldby client device 2002 to server 2001 at high speed using themillimeter-wave communication.

Next, client device 2002 deletes from storage 2012 the sensorinformation that has been transmitted to server 2001 (S2004). It shouldbe noted that when sensor information that has not been transmitted toserver 2001 meets predetermined conditions, client device 2002 maydelete the sensor information. For example, when an obtainment time ofsensor information held by client device 2002 precedes a current time bya certain time, client device 2002 may delete the sensor informationfrom storage 2012. In other words, when a difference between the currenttime and a time when a sensor obtained sensor information exceeds apredetermined time, client device 2002 may delete the sensor informationfrom storage 2012. Besides, when an obtainment location of sensorinformation held by client device 2002 is separated from a currentlocation by a certain distance, client device 2002 may delete the sensorinformation from storage 2012. In other words, when a difference betweena current position of the mobile object or a sensor and a position ofthe mobile object or the sensor when the sensor obtained sensorinformation exceeds a predetermined distance, client device 2002 maydelete the sensor information from storage 2012. Accordingly, it ispossible to reduce the capacity of storage 2012 of client device 2002.

When client device 2002 does not finish obtaining sensor information (NOin S2005), client device 2002 performs step S2001 and the subsequentsteps again. Further, when client device 2002 finishes obtaining sensorinformation (YES in S2005), client device 2002 completes the process.

Client device 2002 may select sensor information to be transmitted toserver 2001, in accordance with communication conditions. For example,when high-speed communication is available, client device 2002preferentially transmits sensor information (e.g., information obtainedby LiDAR) of which the data size held in storage 2012 is large.Additionally, when high-speed communication is not readily available,client device 2002 transmits sensor information (e.g., a visible lightimage) which has high priority and of which the data size held instorage 2012 is small. Accordingly, client device 2002 can efficientlytransmit sensor information held in storage 2012, in accordance withnetwork conditions

Client device 2002 may obtain, from server 2001, time informationindicating a current time and location information indicating a currentlocation. Moreover, client device 2002 may determine an obtainment timeand an obtainment location of sensor information based on the obtainedtime information and location information. In other words, client device2002 may obtain time information from server 2001 and generateobtainment time information using the obtained time information. Clientdevice 2002 may also obtain location information from server 2001 andgenerate obtainment location information using the obtained locationinformation.

For example, regarding time information, server 2001 and client device2002 perform clock synchronization using a means such as the NetworkTime Protocol (NTP) or the Precision Time Protocol (PTP). This enablesclient device 2002 to obtain accurate time information. What's more,since it is possible to synchronize clocks between server 2001 andclient devices 2002, it is possible to synchronize times included inpieces of sensor information obtained by separate client devices 2002.As a result, server 2001 can handle sensor information indicating asynchronized time. It should be noted that a means of synchronizingclocks may be any means other than the NTP or PTP. In addition, GPSinformation may be used as the time information and the locationinformation.

Server 2001 may specify a time or a location and obtain pieces of sensorinformation from client devices 2002. For example, when an accidentoccurs, in order to search for a client device in the vicinity of theaccident, server 2001 specifies an accident occurrence time and anaccident occurrence location and broadcasts sensor informationtransmission requests to client devices 2002. Then, client device 2002having sensor information obtained at the corresponding time andlocation transmits the sensor information to server 2001. In otherwords, client device 2002 receives, from server 2001, a sensorinformation transmission request including specification informationspecifying a location and a time. When sensor information obtained at alocation and a time indicated by the specification information is storedin storage 2012, and client device 2002 determines that the mobileobject is present in the environment in which the mobile object cantransmit the sensor information to server 2001, client device 2002transmits, to server 2001, the sensor information obtained at thelocation and the time indicated by the specification information.Consequently, server 2001 can obtain the pieces of sensor informationpertaining to the occurrence of the accident from client devices 2002,and use the pieces of sensor information for accident analysis etc.

It should be noted that when client device 2002 receives a sensorinformation transmission request from server 2001, client device 2002may refuse to transmit sensor information. Additionally, client device2002 may set in advance which pieces of sensor information can betransmitted. Alternatively, server 2001 may inquire of client device2002 each time whether sensor information can be transmitted.

A point may be given to client device 2002 that has transmitted sensorinformation to server 2001. This point can be used in payment for, forexample, gasoline expenses, electric vehicle (EV) charging expenses, ahighway toll, or rental car expenses. After obtaining sensorinformation, server 2001 may delete information for specifying clientdevice 2002 that has transmitted the sensor information. For example,this information is a network address of client device 2002. Since thisenables the anonymization of sensor information, a user of client device2002 can securely transmit sensor information from client device 2002 toserver 2001. Server 2001 may include servers. For example, by serverssharing sensor information, even when one of the servers breaks down,the other servers can communicate with client device 2002. Accordingly,it is possible to avoid service outage due to a server breakdown.

A specified location specified by a sensor information transmissionrequest indicates an accident occurrence location etc., and may bedifferent from a position of client device 2002 at a specified timespecified by the sensor information transmission request. For thisreason, for example, by specifying, as a specified location, a rangesuch as within XX meters of a surrounding area, server 2001 can requestinformation from client device 2002 within the range. Similarly, server2001 may also specify, as a specified time, a range such as within Nseconds before and after a certain time. As a result, server 2001 canobtain sensor information from client device 2002 present for a timefrom t-N to t+N and in a location within XX meters from absoluteposition S. When client device 2002 transmits three-dimensional datasuch as LiDAR, client device 2002 may transmit data created immediatelyafter time t.

Server 2001 may separately specify information indicating, as aspecified location, a location of client device 2002 from which sensorinformation is to be obtained, and a location at which sensorinformation is desirably obtained. For example, server 2001 specifiesthat sensor information including at least a range within YY meters fromabsolute position S is to be obtained from client device 2002 presentwithin XX meters from absolute position S. When client device 2002selects three-dimensional data to be transmitted, client device 2002selects one or more pieces of three-dimensional data so that the one ormore pieces of three-dimensional data include at least the sensorinformation including the specified range. Each of the one or morepieces of three-dimensional data is a random-accessible unit of data. Inaddition, when client device 2002 transmits a visible light image,client device 2002 may transmit pieces of temporally continuous imagedata including at least a frame immediately before or immediately aftertime t.

When client device 2002 can use physical networks such as 5G, Wi-Fi, ormodes in 5G for transmitting sensor information, client device 2002 mayselect a network to be used according to the order of priority notifiedby server 2001. Alternatively, client device 2002 may select a networkthat enables client device 2002 to ensure an appropriate bandwidth basedon the size of transmit data. Alternatively, client device 2002 mayselect a network to be used, based on data transmission expenses etc. Atransmission request from server 2001 may include information indicatinga transmission deadline, for example, performing transmission whenclient device 2002 can start transmission by time t. When server 2001cannot obtain sufficient sensor information within a time limit, server2001 may issue a transmission request again.

Sensor information may include header information indicatingcharacteristics of sensor data along with compressed or uncompressedsensor data. Client device 2002 may transmit header information toserver 2001 via a physical network or a communication protocol that isdifferent from a physical network or a communication protocol used forsensor data. For example, client device 2002 transmits headerinformation to server 2001 prior to transmitting sensor data. Server2001 determines whether to obtain the sensor data of client device 2002,based on a result of analysis of the header information. For example,header information may include information indicating a point cloudobtainment density, an elevation angle, or a frame rate of LiDAR, orinformation indicating, for example, a resolution, an SN ratio, or aframe rate of a visible light image. Accordingly, server 2001 can obtainthe sensor information from client device 2002 having the sensor data ofdetermined quality.

As stated above, client device 2002 is provided in the mobile object,obtains sensor information that has been obtained by a sensor providedin the mobile object and indicates a surrounding state of the mobileobject, and stores the sensor information into storage 2012. Clientdevice 2002 determines whether the mobile object is present in anenvironment in which the mobile object is capable of transmitting thesensor information to server 2001, and transmits the sensor informationto server 2001 when the mobile object is determined to be present in theenvironment in which the mobile object is capable of transmitting thesensor information to server 2001.

Additionally, client device 2002 further creates, from the sensorinformation, three-dimensional data of a surrounding area of the mobileobject, and estimates a self-location of the mobile object using thethree-dimensional data created.

Besides, client device 2002 further transmits a transmission request fora three-dimensional map to server 2001, and receives thethree-dimensional map from server 2001. In the estimating, client device2002 estimates the self-location using the three-dimensional data andthe three-dimensional map.

It should be noted that the above process performed by client device2002 may be realized as an information transmission method for use inclient device 2002.

In addition, client device 2002 may include a processor and memory.Using the memory, the processor may perform the above process.

Next, a sensor information collection system according to the presentembodiment will be described. FIG. 21 is a diagram illustrating aconfiguration of the sensor information collection system according tothe present embodiment. As illustrated in FIG. 21, the sensorinformation collection system according to the present embodimentincludes terminal 2021A, terminal 2021B, communication device 2022A,communication device 2022B, network 2023, data collection server 2024,map server 2025, and client device 2026. It should be noted that whenterminal 2021A and terminal 2021B are not particularly distinguished,terminal 2021A and terminal 2021B are also referred to as terminal 2021.Additionally, when communication device 2022A and communication device2022B are not particularly distinguished, communication device 2022A andcommunication device 2022B are also referred to as communication device2022.

Data collection server 2024 collects data such as sensor data obtainedby a sensor included in terminal 2021 as position-related data in whichthe data is associated with a position in a three-dimensional space.

Sensor data is data obtained by, for example, detecting a surroundingstate of terminal 2021 or an internal state of terminal 2021 using asensor included in terminal 2021. Terminal 2021 transmits, to datacollection server 2024, one or more pieces of sensor data collected fromone or more sensor devices in locations at which direct communicationwith terminal 2021 is possible or at which communication with terminal2021 is possible by the same communication system or via one or morerelay devices.

Data included in position-related data may include, for example,information indicating an operating state, an operating log, a serviceuse state, etc. of a terminal or a device included in the terminal. Inaddition, the data include in the position-related data may include, forexample, information in which an identifier of terminal 2021 isassociated with a position or a movement path etc. of terminal 2021.

Information indicating a position included in position-related data isassociated with, for example, information indicating a position inthree-dimensional data such as three-dimensional map data. The detailsof information indicating a position will be described later.

Position-related data may include at least one of the above-describedtime information or information indicating an attribute of data includedin the position-related data or a type (e.g., a model number) of asensor that has created the data, in addition to position informationthat is information indicating a position. The position information andthe time information may be stored in a header area of theposition-related data or a header area of a frame that stores theposition-related data. Further, the position information and the timeinformation may be transmitted and/or stored as metadata associated withthe position-related data, separately from the position-related data.

Map server 2025 is connected to, for example, network 2023, andtransmits three-dimensional data such as three-dimensional map data inresponse to a request from another device such as terminal 2021.Besides, as described in the aforementioned embodiments, map server 2025may have, for example, a function of updating three-dimensional datausing sensor information transmitted from terminal 2021.

Data collection server 2024 is connected to, for example, network 2023,collects position-related data from another device such as terminal2021, and stores the collected position-related data into a storage ofdata collection server 2024 or a storage of another server. In addition,data collection server 2024 transmits, for example, metadata ofcollected position-related data or three-dimensional data generatedbased on the position-related data, to terminal 2021 in response to arequest from terminal 2021.

Network 2023 is, for example, a communication network such as theInternet. Terminal 2021 is connected to network 2023 via communicationdevice 2022. Communication device 2022 communicates with terminal 2021using one communication system or switching between communicationsystems. Communication device 2022 is a communication satellite thatperforms communication using, for example, (1) a base station compliantwith Long-Term Evolution (LTE) etc., (2) an access point (AP) for Wi-Fior millimeter-wave communication etc., (3) a low-power wide-area (LPWA)network gateway such as SIGFOX, LoRaWAN, or Wi-SUN, or (4) a satellitecommunication system such as DVB-S2.

It should be noted that a base station may communicate with terminal2021 using a system classified as an LPWA network such as NarrowbandInternet of Things (NB IoT) or LTE-M, or switching between thesesystems.

Here, although, in the example given, terminal 2021 has a function ofcommunicating with communication device 2022 that uses two types ofcommunication systems, and communicates with map server 2025 or datacollection server 2024 using one of the communication systems orswitching between the communication systems and between communicationdevices 2022 to be a direct communication partner; a configuration ofthe sensor information collection system and terminal 2021 is notlimited to this. For example, terminal 2021 need not have a function ofperforming communication using communication systems, and may have afunction of performing communication using one of the communicationsystems. Terminal 2021 may also support three or more communicationsystems. Additionally, each terminal 2021 may support a differentcommunication system.

Terminal 2021 includes, for example, the configuration of client device902 illustrated in FIG. 11. Terminal 2021 estimates a self-location etc.using received three-dimensional data. Besides, terminal 2021 associatessensor data obtained from a sensor and position information obtained byself-location estimation to generate position-related data.

Position information appended to position-related data indicates, forexample, a position in a coordinate system used for three-dimensionaldata. For example, the position information is coordinate valuesrepresented using a value of a latitude and a value of a longitude.Here, terminal 2021 may include, in the position information, acoordinate system serving as a reference for the coordinate values andinformation indicating three-dimensional data used for locationestimation, along with the coordinate values. Coordinate values may alsoinclude altitude information.

The position information may be associated with a data unit or a spaceunit usable for encoding the above three-dimensional data. Such a unitis, for example, WLD, GOS, SPC, VLM, or VXL. Here, the positioninformation is represented by, for example, an identifier foridentifying a data unit such as the SPC corresponding toposition-related data. It should be noted that the position informationmay include, for example, information indicating three-dimensional dataobtained by encoding a three-dimensional space including a data unitsuch as the SPC or information indicating a detailed position within theSPC, in addition to the identifier for identifying the data unit such asthe SPC. The information indicating the three-dimensional data is, forexample, a file name of the three-dimensional data.

As stated above, by generating position-related data associated withposition information based on location estimation usingthree-dimensional data, the system can give more accurate positioninformation to sensor information than when the system appends positioninformation based on a self-location of a client device (terminal 2021)obtained using a GPS to sensor information. As a result, even whenanother device uses the position-related data in another service, thereis a possibility of more accurately determining a position correspondingto the position-related data in an actual space, by performing locationestimation based on the same three-dimensional data.

It should be noted that although the data transmitted from terminal 2021is the position-related data in the example given in the presentembodiment, the data transmitted from terminal 2021 may be dataunassociated with position information. In other words, the transmissionand reception of three-dimensional data or sensor data described in theother embodiments may be performed via network 2023 described in thepresent embodiment.

Next, a different example of position information indicating a positionin a three-dimensional or two-dimensional actual space or in a map spacewill be described. The position information appended to position-relateddata may be information indicating a relative position relative to akeypoint in three-dimensional data. Here, the keypoint serving as areference for the position information is encoded as, for example, SWLD,and notified to terminal 2021 as three-dimensional data.

The information indicating the relative position relative to thekeypoint may be, for example, information that is represented by avector from the keypoint to the point indicated by the positioninformation, and indicates a direction and a distance from the keypointto the point indicated by the position information. Alternatively, theinformation indicating the relative position relative to the keypointmay be information indicating an amount of displacement from thekeypoint to the point indicated by the position information along eachof the x axis, the y axis, and the z axis. Additionally, the informationindicating the relative position relative to the keypoint may beinformation indicating a distance from each of three or more keypointsto the point indicated by the position information. It should be notedthat the relative position need not be a relative position of the pointindicated by the position information represented using each keypoint asa reference, and may be a relative position of each keypoint representedwith respect to the point indicated by the position information.Examples of position information based on a relative position relativeto a keypoint include information for identifying a keypoint to be areference, and information indicating the relative position of the pointindicated by the position information and relative to the keypoint. Whenthe information indicating the relative position relative to thekeypoint is provided separately from three-dimensional data, theinformation indicating the relative position relative to the keypointmay include, for example, coordinate axes used in deriving the relativeposition, information indicating a type of the three-dimensional data,and/or information indicating a magnitude per unit amount (e.g., ascale) of a value of the information indicating the relative position.

The position information may include, for each keypoint, informationindicating a relative position relative to the keypoint. When theposition information is represented by relative positions relative tokeypoints, terminal 2021 that intends to identify a position in anactual space indicated by the position information may calculatecandidate points of the position indicated by the position informationfrom positions of the keypoints each estimated from sensor data, and maydetermine that a point obtained by averaging the calculated candidatepoints is the point indicated by the position information. Since thisconfiguration reduces the effects of errors when the positions of thekeypoints are estimated from the sensor data, it is possible to improvethe estimation accuracy for the point in the actual space indicated bythe position information. Besides, when the position informationincludes information indicating relative positions relative tokeypoints, if it is possible to detect any one of the keypointsregardless of the presence of keypoints undetectable due to a limitationsuch as a type or performance of a sensor included in terminal 2021, itis possible to estimate a value of the point indicated by the positioninformation.

A point identifiable from sensor data can be used as a keypoint.Examples of the point identifiable from the sensor data include a pointor a point within a region that satisfies a predetermined keypointdetection condition, such as the above-described three-dimensionalfeature or feature of visible light data is greater than or equal to athreshold value.

Moreover, a marker etc. placed in an actual space may be used as akeypoint. In this case, the maker may be detected and located from dataobtained using a sensor such as LiDER or a camera. For example, themarker may be represented by a change in color or luminance value(degree of reflection), or a three-dimensional shape (e.g., unevenness).Coordinate values indicating a position of the marker, or atwo-dimensional bar code or a bar code etc. generated from an identifierof the marker may be also used.

Furthermore, a light source that transmits an optical signal may be usedas a marker. When a light source of an optical signal is used as amarker, not only information for obtaining a position such as coordinatevalues or an identifier but also other data may be transmitted using anoptical signal. For example, an optical signal may include contents ofservice corresponding to the position of the marker, an address forobtaining contents such as a URL, or an identifier of a wirelesscommunication device for receiving service, and information indicating awireless communication system etc. for connecting to the wirelesscommunication device. The use of an optical communication device (alight source) as a marker not only facilitates the transmission of dataother than information indicating a position but also makes it possibleto dynamically change the data.

Terminal 2021 finds out a correspondence relationship of keypointsbetween mutually different data using, for example, a common identifierused for the data, or information or a table indicating thecorrespondence relationship of the keypoints between the data. Whenthere is no information indicating a correspondence relationship betweenkeypoints, terminal 2021 may also determine that when coordinates of akeypoint in three-dimensional data are converted into a position in aspace of another three-dimensional data, a keypoint closest to theposition is a corresponding keypoint.

When the position information based on the relative position describedabove is used, terminal 2021 that uses mutually differentthree-dimensional data or services can identify or estimate a positionindicated by the position information with respect to a common keypointincluded in or associated with each three-dimensional data. As a result,terminal 2021 that uses the mutually different three-dimensional data orthe services can identify or estimate the same position with higheraccuracy.

Even when map data or three-dimensional data represented using mutuallydifferent coordinate systems are used, since it is possible to reducethe effects of errors caused by the conversion of a coordinate system,it is possible to coordinate services based on more accurate positioninformation.

Hereinafter, an example of functions provided by data collection server2024 will be described. Data collection server 2024 may transferreceived position-related data to another data server. When there aredata servers, data collection server 2024 determines to which dataserver received position-related data is to be transferred, andtransfers the position-related data to a data server determined as atransfer destination.

Data collection server 2024 determines a transfer destination based on,for example, transfer destination server determination rules preset todata collection server 2024. The transfer destination serverdetermination rules are set by, for example, a transfer destinationtable in which identifiers respectively associated with terminals 2021are associated with transfer destination data servers.

Terminal 2021 appends an identifier associated with terminal 2021 toposition-related data to be transmitted, and transmits theposition-related data to data collection server 2024. Data collectionserver 2024 determines a transfer destination data server correspondingto the identifier appended to the position-related data, based on thetransfer destination server determination rules set out using thetransfer destination table etc.; and transmits the position-related datato the determined data server. The transfer destination serverdetermination rules may be specified based on a determination conditionset using a time, a place, etc. at which position-related data isobtained. Here, examples of the identifier associated with transmissionsource terminal 2021 include an identifier unique to each terminal 2021or an identifier indicating a group to which terminal 2021 belongs.

The transfer destination table need not be a table in which identifiersassociated with transmission source terminals are directly associatedwith transfer destination data servers. For example, data collectionserver 2024 holds a management table that stores tag informationassigned to each identifier unique to terminal 2021, and a transferdestination table in which the pieces of tag information are associatedwith transfer destination data servers. Data collection server 2024 maydetermine a transfer destination data server based on tag information,using the management table and the transfer destination table. Here, thetag information is, for example, control information for management orcontrol information for providing service assigned to a type, a modelnumber, an owner of terminal 2021 corresponding to the identifier, agroup to which terminal 2021 belongs, or another identifier. Moreover,in the transfer destination able, identifiers unique to respectivesensors may be used instead of the identifiers associated withtransmission source terminals 2021. Furthermore, the transferdestination server determination rules may be set by client device 2026.

Data collection server 2024 may determine data servers as transferdestinations, and transfer received position-related data to the dataservers. According to this configuration, for example, whenposition-related data is automatically backed up or when, in order thatposition-related data is commonly used by different services, there is aneed to transmit the position-related data to a data server forproviding each service, it is possible to achieve data transfer asintended by changing a setting of data collection server 2024. As aresult, it is possible to reduce the number of steps necessary forbuilding and changing a system, compared to when a transmissiondestination of position-related data is set for each terminal 2021.

Data collection server 2024 may register, as a new transfer destination,a data server specified by a transfer request signal received from adata server; and transmit position-related data subsequently received tothe data server, in response to the transfer request signal.

Data collection server 2024 may store position-related data receivedfrom terminal 2021 into a recording device, and transmitposition-related data specified by a transmission request signalreceived from terminal 2021 or a data server to request source terminal2021 or the data server in response to the transmission request signal.

Data collection server 2024 may determine whether position-related datais suppliable to a request source data server or terminal 2021, andtransfer or transmit the position-related data to the request sourcedata server or terminal 2021 when determining that the position-relateddata is suppliable.

When data collection server 2024 receives a request for currentposition-related data from client device 2026, even if it is not atiming for transmitting position-related data by terminal 2021, datacollection server 2024 may send a transmission request for the currentposition-related data to terminal 2021, and terminal 2021 may transmitthe current position-related data in response to the transmissionrequest.

Although terminal 2021 transmits position information data to datacollection server 2024 in the above description, data collection server2024 may have a function of managing terminal 2021 such as a functionnecessary for collecting position-related data from terminal 2021 or afunction used when collecting position-related data from terminal 2021.

Data collection server 2024 may have a function of transmitting, toterminal 2021, a data request signal for requesting transmission ofposition information data, and collecting position-related data.

Management information such as an address for communicating withterminal 2021 from which data is to be collected or an identifier uniqueto terminal 2021 is registered in advance in data collection server2024. Data collection server 2024 collects position-related data fromterminal 2021 based on the registered management information. Managementinformation may include information such as types of sensors included interminal 2021, the number of sensors included in terminal 2021, andcommunication systems supported by terminal 2021.

Data collection server 2024 may collect information such as an operatingstate or a current position of terminal 2021 from terminal 2021.

Registration of management information may be instructed by clientdevice 2026, or a process for the registration may be started byterminal 2021 transmitting a registration request to data collectionserver 2024. Data collection server 2024 may have a function ofcontrolling communication between data collection server 2024 andterminal 2021.

Communication between data collection server 2024 and terminal 2021 maybe established using a dedicated line provided by a service providersuch as a mobile network operator (MNO) or a mobile virtual networkoperator (MVNO), or a virtual dedicated line based on a virtual privatenetwork (VPN). According to this configuration, it is possible toperform secure communication between terminal 2021 and data collectionserver 2024.

Data collection server 2024 may have a function of authenticatingterminal 2021 or a function of encrypting data to be transmitted andreceived between data collection server 2024 and terminal 2021. Here,the authentication of terminal 2021 or the encryption of data isperformed using, for example, an identifier unique to terminal 2021 oran identifier unique to a terminal group including terminals 2021, whichis shared in advance between data collection server 2024 and terminal2021. Examples of the identifier include an international mobilesubscriber identity (IMSI) that is a unique number stored in asubscriber identity module (SIM) card. An identifier for use inauthentication and an identifier for use in encryption of data may beidentical or different.

The authentication or the encryption of data between data collectionserver 2024 and terminal 2021 is feasible when both data collectionserver 2024 and terminal 2021 have a function of performing the process.The process does not depend on a communication system used bycommunication device 2022 that performs relay. Accordingly, since it ispossible to perform the common authentication or encryption withoutconsidering whether terminal 2021 uses a communication system, theuser's convenience of system architecture is increased. However, theexpression “does not depend on a communication system used bycommunication device 2022 that performs relay” means a change accordingto a communication system is not essential. In other words, in order toimprove the transfer efficiency or ensure security, the authenticationor the encryption of data between data collection server 2024 andterminal 2021 may be changed according to a communication system used bya relay device.

Data collection server 2024 may provide client device 2026 with a UserInterface (UI) that manages data collection rules such as types ofposition-related data collected from terminal 2021 and data collectionschedules. Accordingly, a user can specify, for example, terminal 2021from which data is to be collected using client device 2026, a datacollection time, and a data collection frequency. Additionally, datacollection server 2024 may specify, for example, a region on a map fromwhich data is to be desirably collected, and collect position-relateddata from terminal 2021 included in the region.

When the data collection rules are managed on a per terminal 2021 basis,client device 2026 presents, on a screen, a list of terminals 2021 orsensors to be managed. The user sets, for example, a necessity for datacollection or a collection schedule for each item in the list.

When a region on a map from which data is to be desirably collected isspecified, client device 2026 presents, on a screen, a two-dimensionalor three-dimensional map of a region to be managed. The user selects theregion from which data is to be collected on the displayed map. Examplesof the region selected on the map include a circular or rectangularregion having a point specified on the map as the center, or a circularor rectangular region specifiable by a drag operation. Client device2026 may also select a region in a preset unit such as a city, an areaor a block in a city, or a main road, etc. Instead of specifying aregion using a map, a region may be set by inputting values of alatitude and a longitude, or a region may be selected from a list ofcandidate regions derived based on inputted text information. Textinformation is, for example, a name of a region, a city, or a landmark.

Moreover, data may be collected while the user dynamically changes aspecified region by specifying one or more terminals 2021 and setting acondition such as within 100 meters of one or more terminals 2021.

When client device 2026 includes a sensor such as a camera, a region ona map may be specified based on a position of client device 2026 in anactual space obtained from sensor data. For example, client device 2026may estimate a self-location using sensor data, and specify, as a regionfrom which data is to be collected, a region within a predetermineddistance from a point on a map corresponding to the estimated locationor a region within a distance specified by the user. Client device 2026may also specify, as the region from which the data is to be collected,a sensing region of the sensor, that is, a region corresponding toobtained sensor data. Alternatively, client device 2026 may specify, asthe region from which the data is to be collected, a region based on alocation corresponding to sensor data specified by the user. Eitherclient device 2026 or data collection server 2024 may estimate a regionon a map or a location corresponding to sensor data.

When a region on a map is specified, data collection server 2024 mayspecify terminal 2021 within the specified region by collecting currentposition information of each terminal 2021, and may send a transmissionrequest for position-related data to specified terminal 2021. When datacollection server 2024 transmits information indicating a specifiedregion to terminal 2021, determines whether terminal 2021 is presentwithin the specified region, and determines that terminal 2021 ispresent within the specified region, rather than specifying terminal2021 within the region, terminal 2021 may transmit position-relateddata.

Data collection server 2024 transmits, to client device 2026, data suchas a list or a map for providing the above-described User Interface (UI)in an application executed by client device 2026. Data collection server2024 may transmit, to client device 2026, not only the data such as thelist or the map but also an application program. Additionally, the aboveUI may be provided as contents created using HTML displayable by abrowser. It should be noted that part of data such as map data may besupplied from a server, such as map server 2025, other than datacollection server 2024.

When client device 2026 receives an input for notifying the completionof an input such as pressing of a setup key by the user, client device2026 transmits the inputted information as configuration information todata collection server 2024. Data collection server 2024 transmits, toeach terminal 2021, a signal for requesting position-related data ornotifying position-related data collection rules, based on theconfiguration information received from client device 2026, and collectsthe position-related data.

Next, an example of controlling operation of terminal 2021 based onadditional information added to three-dimensional or two-dimensional mapdata will be described.

In the present configuration, object information that indicates aposition of a power feeding part such as a feeder antenna or a feedercoil for wireless power feeding buried under a road or a parking lot isincluded in or associated with three-dimensional data, and such objectinformation is provided to terminal 2021 that is a vehicle or a drone.

A vehicle or a drone that has obtained the object information to getcharged automatically moves so that a position of a charging part suchas a charging antenna or a charging coil included in the vehicle or thedrone becomes opposite to a region indicated by the object information,and such vehicle or a drone starts to charge itself. It should be notedthat when a vehicle or a drone has no automatic driving function, adirection to move in or an operation to perform is presented to a driveror an operator by using an image displayed on a screen, audio, etc. Whena position of a charging part calculated based on an estimatedself-location is determined to fall within the region indicated by theobject information or a predetermined distance from the region, an imageor audio to be presented is changed to a content that puts a stop todriving or operating, and the charging is started.

Object information need not be information indicating a position of apower feeding part, and may be information indicating a region withinwhich placement of a charging part results in a charging efficiencygreater than or equal to a predetermined threshold value. A positionindicated by object information may be represented by, for example, thecentral point of a region indicated by the object information, a regionor a line within a two-dimensional plane, or a region, a line, or aplane within a three-dimensional space.

According to this configuration, since it is possible to identify theposition of the power feeding antenna unidentifiable by sensing data ofLiDER or an image captured by the camera, it is possible to highlyaccurately align a wireless charging antenna included in terminal 2021such as a vehicle with a wireless power feeding antenna buried under aroad. As a result, it is possible to increase a charging speed at thetime of wireless charging and improve the charging efficiency.

Object information may be an object other than a power feeding antenna.For example, three-dimensional data includes, for example, a position ofan AP for millimeter-wave wireless communication as object information.Accordingly, since terminal 2021 can identify the position of the AP inadvance, terminal 2021 can steer a directivity of beam to a direction ofthe object information and start communication. As a result, it ispossible to improve communication quality such as increasingtransmission rates, reducing the duration of time before startingcommunication, and extending a communicable period.

Object information may include information indicating a type of anobject corresponding to the object information. In addition, whenterminal 2021 is present within a region in an actual spacecorresponding to a position in three-dimensional data of the objectinformation or within a predetermined distance from the region, theobject information may include information indicating a process to beperformed by terminal 2021.

Object information may be provided by a server different from a serverthat provides three-dimensional data. When object information isprovided separately from three-dimensional data, object groups in whichobject information used by the same service is stored may be eachprovided as separate data according to a type of a target service or atarget device.

Three-dimensional data used in combination with object information maybe point cloud data of WLD or keypoint data of SWLD.

Embodiment 6

Hereinafter, a scan order of an octree representation and voxels will bedescribed. A volume is encoded after being converted into an octreestructure (made into an octree). The octree structure includes nodes andleaves. Each node has eight nodes or leaves, and each leaf has voxel(VXL) information. FIG. 22 is a diagram showing an example structure ofa volume including voxels. FIG. 23 is a diagram showing an example ofthe volume shown in FIG. 22 having been converted into the octreestructure. Among the leaves shown in FIG. 23, leaves 1, 2, and 3respectively represent VXL 1, VXL 2, and VXL 3, and represent VXLsincluding a point group (hereinafter, active VXLs).

An octree is represented by, for example, binary sequences of 1s and 0s.For example, when giving the nodes or the active VXLs a value of 1 andeverything else a value of 0, each node and leaf is assigned with thebinary sequence shown in FIG. 23. Thus, this binary sequence is scannedin accordance with a breadth-first or a depth-first scan order. Forexample, when scanning breadth-first, the binary sequence shown in A ofFIG. 24 is obtained. When scanning depth-first, the binary sequenceshown in B of FIG. 24 is obtained. The binary sequences obtained throughthis scanning are encoded through entropy encoding, which reduces anamount of information.

Depth information in the octree representation will be described next.Depth in the octree representation is used in order to control up to howfine a granularity point cloud information included in a volume isstored. Upon setting a great depth, it is possible to reproduce thepoint cloud information to a more precise level, but an amount of datafor representing the nodes and leaves increases. Upon setting a smalldepth, however, the amount of data decreases, but some information thatthe point cloud information originally held is lost, since pieces ofpoint cloud information including different positions and differentcolors are now considered as pieces of point cloud information includingthe same position and the same color.

For example, FIG. 25 is a diagram showing an example in which the octreewith a depth of 2 shown in FIG. 23 is represented with a depth of 1. Theoctree shown in FIG. 25 has a lower amount of data than the octree shownin FIG. 23. In other words, the binarized octree shown in FIG. 25 has alower bit count than the octree shown in FIG. 23. Leaf 1 and leaf 2shown in FIG. 23 are represented by leaf 1 shown in FIG. 24. In otherwords, the information on leaf 1 and leaf 2 being in different positionsis lost.

FIG. 26 is a diagram showing a volume corresponding to the octree shownin FIG. 25. VXL 1 and VXL 2 shown in FIG. 22 correspond to VXL 12 shownin FIG. 26. In this case, the three-dimensional data encoding devicegenerates color information of VXL 12 shown in FIG. 26 using colorinformation of VXL 1 and VXL 2 shown in FIG. 22. For example, thethree-dimensional data encoding device calculates an average value, amedian, a weighted average value, or the like of the color informationof VXL 1 and VXL 2 as the color information of VXL 12. In this manner,the three-dimensional data encoding device may control a reduction ofthe amount of data by changing the depth of the octree.

The three-dimensional data encoding device may set the depth informationof the octree to units of worlds, units of spaces, or units of volumes.In this case, the three-dimensional data encoding device may append thedepth information to header information of the world, header informationof the space, or header information of the volume. In all worlds,spaces, and volumes associated with different times, the same value maybe used as the depth information. In this case, the three-dimensionaldata encoding device may append the depth information to headerinformation managing the worlds associated with all times.

Embodiment 7

Hereinafter, a method using a RAHT (Region Adaptive HierarchicalTransform) will be described as another method of encoding the attributeinformation of a three-dimensional point. FIG. 27 is a diagram fordescribing the encoding of the attribute information by using a RAHT.

First, the three-dimensional data encoding device generates Morton codesbased on the geometry information of three-dimensional points, and sortsthe attribute information of the three-dimensional points in the orderof the Morton codes. For example, the three-dimensional data encodingdevice may perform sorting in the ascending order of the Morton codes.Note that the sorting order is not limited to the order of the Mortoncodes, and other orders may be used.

Next, the three-dimensional data encoding device generates ahigh-frequency component and a low-frequency component of the layer L byapplying the Haar conversion to the attribute information of twoadjacent three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional data encoding device may use the Haarconversion of 2×2 matrices. The generated high-frequency component isincluded in a coding coefficient as the high-frequency component of thelayer L, and the generated low-frequency component is used as the inputvalue for the higher layer L+1 of the layer L.

After generating the high-frequency component of the layer L by usingthe attribute information of the layer L, the three-dimensional dataencoding device subsequently performs processing of the layer L+1. Inthe processing of the layer L+1, the three-dimensional data encodingdevice generates a high-frequency component and a low-frequencycomponent of the layer L+1 by applying the Haar conversion to twolow-frequency components obtained by the Haar conversion of theattribute information of the layer L. The generated high-frequencycomponent is included in a coding coefficient as the high-frequencycomponent of the layer L+1, and the generated low-frequency component isused as the input value for the higher layer L+2 of the layer L+1.

The three-dimensional data encoding device repeats such layerprocessing, and determines that the highest layer Lmax has been reachedat the time when a low-frequency component that is input to a layerbecomes one. The three-dimensional data encoding device includes thelow-frequency component of the layer Lmax−1 that is input to the LayerLmax in a coding coefficient. Then, the value of the low-frequencycomponent or high-frequency component included in the coding coefficientis quantized, and is encoded by using entropy encoding or the like.

Note that, when only one three-dimensional point exists as two adjacentthree-dimensional points at the time of application of the Haarconversion, the three-dimensional data encoding device may use the valueof the attribute information of the existing one three-dimensional pointas the input value for a higher layer.

In this manner, the three-dimensional data encoding devicehierarchically applies the Haar conversion to the input attributeinformation, generates a high-frequency component and a low-frequencycomponent of the attribute information, and performs encoding byapplying quantization described later or the like. Accordingly, thecoding efficiency can be improved.

When the attribute information is N dimensional, the three-dimensionaldata encoding device may independently apply the Haar conversion foreach dimension, and may calculate each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data encoding device applies the Haarconversion for each component, and calculates each coding coefficient.

The three-dimensional data encoding device may apply the Haar conversionin the order of the layers L, L+1, . . . , Lmax. The closer to the layerLmax, a coding coefficient including the more low-frequency componentsof the input attribute information is generated.

w0 and w1 shown in FIG. 27 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional dataencoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theHaar conversion is applied, or the like. For example, thethree-dimensional data encoding device may improve the coding efficiencysuch that the closer the distance, the greater the weight. Note that thethree-dimensional data encoding device may calculate this weight withanother technique, or need not use the weight.

In the example shown in FIG. 27, the pieces of the input attributeinformation are a0, a1, a2, a3, a4, and a5. Additionally, Ta1, Ta5, Tb1,Tb3, Tc1, and d0 are encoded among the coding coefficients after theHaar conversion. The other coding coefficients (b0, b2, c0 and the like)are medians, and are not encoded.

Specifically, in the example shown in FIG. 27, the high-frequencycomponent Ta1 and the low-frequency component b0 are generated byperforming the Haar conversion on a0 and a1. Here, when the weights w0and w1 are equal, the low-frequency component b0 is the average value ofa0 and a1, and the high-frequency component Ta1 is the differencebetween a0 and a1.

Since there is no attribute information to be paired with a2, a2 is usedas b1 as is. Similarly, since there is no attribute information to bepaired with a3, a3 is used as b2 as is. Additionally, the high-frequencycomponent Ta5 and the low-frequency component b3 are generated byperforming the Haar conversion on a4 and a5.

In the layer L+1, the high-frequency component Tb1 and the low-frequencycomponent c0 are generated by performing the Haar conversion on b0 andb1. Similarly, the high-frequency component Tb3 and the low-frequencycomponent c1 are generated by performing the Haar conversion on b2 andb3.

In the layer Lmax-1, the High-frequency component Tc1 and thelow-frequency component d0 are generated by performing the Haarconversion on c0 and el.

The three-dimensional data encoding device may encode the codingcoefficients to which the Haar conversion has been applied, afterquantizing the coding coefficients. For example, the three-dimensionaldata encoding device performs quantization by dividing the codingcoefficient by the quantization scale (also called the quantization step(QS)). In this case, the smaller the quantization scale, the smaller theerror (quantization error) that may occur due to quantization.Conversely, the larger the quantization scale, the larger thequantization error.

Note that the three-dimensional data encoding device may change thevalue of the quantization scale for each layer. FIG. 28 is a diagramshowing an example of setting the quantization scale for each layer. Forexample, the three-dimensional data encoding device sets smallerquantization scales to the higher layers, and larger quantization scalesto the lower layers. Since the coding coefficients of thethree-dimensional points belonging to the higher layers include morelow-frequency components than the lower layers, there is a highpossibility that the coding coefficients are important components inhuman visual characteristics and the like. Therefore, by suppressing thequantization error that may occur in the higher layers by making thequantization scales for the higher layers small, visual deteriorationcan be suppressed, and the coding efficiency can be improved.

Note that the three-dimensional data encoding device may add thequantization scale for each layer to a header or the like. Accordingly,the three-dimensional decoding device can correctly decode thequantization scale, and can appropriately decode a bitstream.

Additionally, the three-dimensional data encoding device may adaptivelyswitch the value of the quantization scale according to the importanceof a current three-dimensional point to be encoded. For example, thethree-dimensional data encoding device uses a small quantization scalefor a three-dimensional point with high importance, and uses a largequantization scale for a three-dimensional point with low importance.For example, the three-dimensional data encoding device may calculatethe importance from the weight at the time of the Haar conversion, orthe like. For example, the three-dimensional data encoding device maycalculate the quantization scale by using the sum of w0 and w1. In thismanner, by making the quantization scale of a three-dimensional pointwith high importance small, the quantization error becomes small, andthe coding efficiency can be improved.

Additionally, the value of the QS may be made smaller for the higherlayers. Accordingly, the higher the layer, the larger the value of theQW, and the prediction efficiency can be improved by suppressing thequantization error of the three-dimensional point.

Here, a coding coefficient Ta1q after quantization of the codingcoefficient Ta1 of the attribute information a1 is represented byTa1/QS_L. Note that QS may be the same value in all the layers or a partof the layers.

The QW (Quantization Weight) is the value that represents the importanceof a current three-dimensional point to be encoded. For example, theabove-described sum of w0 and w1 may be used as the QW. Accordingly, thehigher the layer, the larger the value of the QW, and the predictionefficiency can be improved by suppressing the quantization error of thethree-dimensional point.

For example, the three-dimensional data encoding device may firstinitialize the values of the QWs of all the three-dimensional pointswith 1, and may update the QW of each three-dimensional point by usingthe values of w0 and w1 at the time of the Haar conversion.Alternatively, the three-dimensional data encoding device may change theinitial value according to the layers, without initializing the valuesof the QWs of all the three-dimensional points with a value of 1. Forexample, the quantization scales for the higher layers becomes small bysetting larger QW initial values for the higher layers. Accordingly,since the prediction error in the higher layers can be suppressed, theprediction accuracy of the lower layers can be increased, and the codingefficiency can be improved. Note that the three-dimensional dataencoding device need not necessarily use the QW.

When using the QW, the quantized value Ta1q of Ta1 is calculated by(Formula K1) and (Formula K2).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack & \; \\{{{Ta}\; 1q} = {\frac{{{Ta}\; 1} + \frac{QS\_ L}{2}}{{QS\_ LoD}\; 1} \times {QWTa}\; 1}} & \left( {{Formula}\mspace{14mu}{K1}} \right) \\{{{QWTa}\; 1} = {1 + {\sum\limits_{i = 0}^{1}w_{i}}}} & \left( {{Formula}\mspace{14mu}{K2}} \right)\end{matrix}$

Additionally, the three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) afterquantization in a certain order. For example, the three-dimensional dataencoding device encodes a plurality of three-dimensional points from thethree-dimensional points included in the higher layers toward the lowerlayers in order.

For example, in the example shown in FIG. 27, the three-dimensional dataencoding device encodes a plurality of three-dimensional points in theorder of Tc1q Tb1q, Tb3q, Ta1q, and Ta5q from d0q included in the higherlayer Lmax. Here, there is a tendency that the lower the layer L, themore likely it is that the coding coefficient after quantization becomes0. This can be due to the following and the like.

Since the coding coefficient of the lower layer L shows a higherfrequency component than the higher layers, there is a tendency that thecoding coefficient becomes 0 depending on a current three-dimensionalpoint. Additionally, by switching the quantization scale according tothe above-described importance or the like, the lower the layer, thelarger the quantization scales, and the more likely it is that thecoding coefficient after quantization becomes 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after quantization becomes 0, and the value αconsecutively occurs in the first code sequence. FIG. 29 is a diagramshowing an example of the first code sequence and the second codesequence.

The three-dimensional data encoding device counts the number of timesthat the value 0 occurs in the first code sequence, and encodes thenumber of times that the value 0 consecutively occurs, instead of theconsecutive values 0. That is, the three-dimensional data encodingdevice generates a second code sequence by replacing the codingcoefficient of the consecutive values 0 in the first code sequence withthe number of consecutive times (ZeroCnt) of 0. Accordingly, when thereare consecutive values 0 of the coding coefficients after quantization,the coding efficiency can be improved by encoding the number ofconsecutive times of 0, rather than encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may entropyencode the value of ZeroCnt. For example, the three-dimensional dataencoding device binarizes the value of ZeroCnt with the truncated unarycode of the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after the binarization. FIG. 30 is adiagram showing an example of the truncated unary code in the case wherethe total number of encoded three-dimensional points is T. At this time,the three-dimensional data encoding device may improve the codingefficiency by using a different coding table for each bit. For example,the three-dimensional data encoding device uses coding table 1 for thefirst bit, uses coding table 2 for the second bit, and coding table 3for the subsequent bits. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by switching thecoding table for each bit.

Additionally, the three-dimensional data encoding device mayarithmetically encode ZeroCnt after binarizing ZeroCnt with anExponential-Golomb. Accordingly, when the value of ZeroCnt easilybecomes large, the efficiency can be more improved than the binarizedarithmetic encoding with the truncated unary code. Note that thethree-dimensional data encoding device may add a flag for switchingbetween using the truncated unary code and using the Exponential-Golombto a header. Accordingly, the three-dimensional data encoding device canimprove the coding efficiency by selecting the optimum binarizationmethod. Additionally, the three-dimensional data decoding device cancorrectly decode a bitstream by referring to the flag included in theheader to switch the binarization method.

The three-dimensional decoding device may convert the decoded codingcoefficient after the quantization from an unsigned integer value to asigned integer value with a method contrary to the method performed bythe three-dimensional data encoding device. Accordingly, when the codingcoefficient is entropy encoded, the three-dimensional decoding devicecan appropriately decode a bitstream generated without considering theoccurrence of a negative integer. Note that the three-dimensionaldecoding device does not necessarily need to convert the codingcoefficient from an unsigned integer value to a signed integer value.For example, when decoding a bitstream including an encoded bit that hasbeen separately entropy encoded, the three-dimensional decoding devicemay decode the sign bit.

The three-dimensional decoding device decodes the coding coefficientafter the quantization converted to the signed integer value, by theinverse quantization and the inverse Haar conversion. Additionally, thethree-dimensional decoding device utilizes the coding coefficient afterthe decoding for the prediction after the current three-dimensionalpoint to be decoded. Specifically, the three-dimensional decoding devicecalculates the inverse quantized value by multiplying the codingcoefficient after the quantization by the decoded quantization scale.Next, the three-dimensional decoding device obtains the decoded value byapplying the inverse Haar conversion described later to the inversequantized value.

For example, the three-dimensional decoding device converts the decodedunsigned integer value to a signed integer value with the followingmethod. When the LSB (least significant bit) of the decoded unsignedinteger value a2u is 1, the signed integer value Ta1q is set to−((a2u+1)>>1). When the LSB of the decoded unsigned integer value a2u isnot 1 (when it is 0), the signed integer value Ta1q is set to (a2u>>1).

Additionally, the inverse quantized value of Ta1 is represented byTa1q×QS_L. Here, Ta1q is the quantized value of Ta1. In addition, QS_Lis the quantization step for the layer L.

Additionally, the QS may be the same value for all the layers or a partof the layers. In addition, the three-dimensional data encoding devicemay add the information indicating the QS to a header or the like.Accordingly, the three-dimensional decoding device can correctly performinverse quantization by using the same QS as the QS used by thethree-dimensional data encoding device.

Next, the inverse Haar conversion will be described. FIG. 31 is adiagram for describing the inverse Haar conversion. Thethree-dimensional decoding device decodes the attribute value of athree-dimensional point by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization.

First, the three-dimensional decoding device generates the Morton codesbased on the geometry information of three-dimensional points, and sortsthe three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional decoding device may perform the sortingin ascending order of the Morton codes. Note that the sorting order isnot limited to the order of the Morton codes, and the other order may beused.

Next, the three-dimensional decoding device restores the attributeinformation of three-dimensional points that are adjacent to each otherin the order of the Morton codes in the layer L, by applying the inverseHaar conversion to the coding coefficient including the low-frequencycomponent of the layer L+1, and the coding coefficient including thehigh-frequency component of the layer L. For example, thethree-dimensional decoding device may use the inverse Haar conversion ofa 2×2 matrix. The attribute information of the restored layer L is usedas the input value for the lower layer L−1.

The three-dimensional decoding device repeats such layer processing, andends the processing when all the attribute information of the bottomlayer is decoded. Note that, when only one three-dimensional pointexists as two three-dimensional points that are adjacent to each otherin the layer L−1 at the time of application of the inverse Haarconversion, the three-dimensional decoding device may assign the valueof the encoding component of the layer L to the attribute value of theone existing three-dimensional point. Accordingly, the three-dimensionaldecoding device can correctly decode a bitstream with improved codingefficiency by applying the Haar conversion to all the values of theinput attribute information.

When the attribute information is N dimensional, the three-dimensionaldecoding device may independently apply the inverse Haar conversion foreach dimension, and may decode each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data decoding device applies the inverseHaar conversion to the coding coefficient for each component, anddecodes each attribute value.

The three-dimensional decoding device may apply the inverse Haarconversion in the order of Layers Lmax, L+1, . . . , L. Additionally, w0and w1 shown in FIG. 31 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional datadecoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theinverse Haar conversion is applied, or the like. For example, thethree-dimensional data encoding device may decode a bitstream withimproved coding efficiency such that the closer the distance, thegreater the weight.

In the example shown in FIG. 31, the coding coefficients after theinverse quantization are Ta1, Ta5, Tb1, Tb3, Tc1, and d0, and a0, a1,a2, a3, a4, and a5 are obtained as the decoded values.

FIG. 32 is a diagram showing a syntax example of the attributeinformation (attribute_data). The attribute information (attribute_data)includes the number of consecutive zeros (ZeroCnt), the number ofattribute dimensions (attribute_dimension), and the coding coefficient(value [j] [i]).

The number of consecutive zeros (ZeroCnt) indicates the number of timesthat the value 0 continues in the coding coefficient after quantization.Note that the three-dimensional data encoding device may arithmeticallyencode ZeroCnt after binarizing ZeroCnt.

Additionally, as shown in FIG. 32, the three-dimensional data encodingdevice may determine whether or not the layer L (layerL) to which thecoding coefficient belongs is equal to or more than a predefinedthreshold value TH_layer, and may switch the information added to abitstream according to the determination result. For example, when thedetermination result is true, the three-dimensional data encoding deviceadds all the coding coefficients of the attribute information to abitstream. In addition, when the determination result is false, thethree-dimensional data encoding device may add a part of the codingcoefficients to a bitstream.

Specifically, when the determination result is true, thethree-dimensional data encoding device adds the encoded result of thethree-dimensional information of the color information RGB or YUV to abitstream. When the determination result is false, the three-dimensionaldata encoding device may add a part of information such as G or Y of thecolor information to a bitstream, and need not to add the othercomponents to the bitstream. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by not adding a partof the coding coefficients of the layer (the layer smaller thanTH_layer) including the coding coefficients indicating thehigh-frequency component with less visually noticeable degradation to abitstream.

The number of attribute dimensions (attribute_dimension) indicates thenumber of dimensions of the attribute information. For example, when theattribute information is the color information (RGB, YUV, or the like)of a three-dimensional point, since the color information isthree-dimensional, the number of attribute dimensions is set to a value3. When the attribute information is the reflectance, since thereflectance is one-dimensional, the number of attribute dimensions isset to a value 1. Note that the number of attribute dimensions may beadded to the header of the attribute information of a bit stream or thelike.

The coding coefficient (value W [i]) indicates the coding coefficientafter quantization of the attribute information of the j-th dimension ofthe i-th three-dimensional point. For example, when the attributeinformation is color information, value [99] [1] indicates the codingcoefficient of the second dimension (for example, the G value) of the100th three-dimensional point. Additionally, when the attributeinformation is reflectance information, value [119] [0] indicates thecoding coefficient of the first dimension (for example, the reflectance)of the 120th three-dimensional point.

Note that, when the following conditions are satisfied, thethree-dimensional data encoding device may subtract the value 1 fromvalue W [i], and may entropy encode the obtained value. In this case,the three-dimensional data decoding device restores the codingcoefficient by adding the value 1 to value W [i] after entropy decoding.

The above-described conditions are (1) when attribute_dimension=1, or(2) when attribute_dimension is 1 or more, and when the values of allthe dimensions are equal. For example, when the attribute information isthe reflectance, since attribute_dimension=1, the three-dimensional dataencoding device subtracts the value 1 from the coding coefficient tocalculate value, and encodes the calculated value. The three-dimensionaldecoding device calculates the coding coefficient by adding the value 1to the value after decoding.

More specifically, for example, when the coding coefficient of thereflectance is 10, the three-dimensional data encoding device encodesthe value 9 obtained by subtracting the value 1 from the value 10 of thecoding coefficient. The three-dimensional data decoding device adds thevalue 1 to the decoded value 9 to calculate the value 10 of the codingcoefficient.

Additionally, since attribute_dimension=3 when the attribute informationis the color, for example, when the coding coefficient afterquantization of each of the components R, G, and B is the same, thethree-dimensional data encoding device subtracts the value 1 from eachcoding coefficient, and encodes the obtained value. Thethree-dimensional data decoding device adds the value 1 to the valueafter decoding. More specifically, for example, when the codingcoefficient of R, G, and B=(1, 1, 1), the three-dimensional dataencoding device encodes (0, 0, 0). The three-dimensional data decodingdevice adds 1 to each component of (0, 0, 0) to calculate (1, 1, 1).Additionally, when the coding coefficients of R, G, and B=(2, 1, 2), thethree-dimensional data encoding device encodes (2, 1, 2) as is. Thethree-dimensional data decoding device uses the decoded (2, 1, 2) as isas the coding coefficients.

In this manner, by providing ZeroCnt, since the pattern in which all thedimensions are 0 as value is not generated, the value obtained bysubtracting 1 from the value indicated by value can be encoded.Therefore, the coding efficiency can be improved.

Additionally, value [0] [i] shown in FIG. 32 indicates the codingcoefficient after quantization of the attribute information of the firstdimension of the i-th three-dimensional point. As shown in FIG. 32, whenthe layer L (layerL) to which the coding coefficient belongs is smallerthan the threshold value TH_layer, the code amount may be reduced byadding the attribute information of the first dimension to a bitstream(not adding the attribute information of the second and followingdimensions to the bitstream). The three-dimensional data encoding devicemay switch the calculation method of the value of ZeroCnt depending onthe value of attribute_dimension. For example, whenattribute_dimension=3, the three-dimensional data encoding device maycount the number of times that the values of the coding coefficients ofall the components (dimensions) become 0. FIG. 33 is a diagram showingan example of the coding coefficient and ZeroCnt in this case. Forexample, in the case of the color information shown in FIG. 33, thethree-dimensional data encoding device counts the number of theconsecutive coding coefficients having 0 for all of the R, G, and Bcomponents, and adds the counted number to a bitstream as ZeroCnt.Accordingly, it becomes unnecessary to encode ZeroCnt for eachcomponent, and the overhead can be reduced. Therefore, the codingefficiency can be improved. Note that the three-dimensional dataencoding device may calculate ZeroCnt for each dimension even whenattribute_dimension is two or more, and may add the calculated ZeroCntto a bitstream.

FIG. 34 is a flowchart of the three-dimensional data encoding processingaccording to the present embodiment. First, the three-dimensional dataencoding device encodes geometry information (geometry) (S6601). Forexample, the three-dimensional data encoding device performs encoding byusing an octree representation.

Next, the three-dimensional data encoding device converts the attributeinformation (S6602). For example, after the encoding of the geometryinformation, when the position of a three-dimensional point is changeddue to quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information of the originalthree-dimensional point to the three-dimensional point after the change.Note that the three-dimensional data encoding device may interpolate thevalue of the attribute information according to the amount of change ofthe position to perform the reassignment. For example, thethree-dimensional data encoding device detects N three-dimensionalpoints before the change near the three dimensional position after thechange, performs the weighted averaging of the value of the attributeinformation of the N three-dimensional points based on the distance fromthe three-dimensional position after the change to each of the Nthree-dimensional points, and sets the obtained value as the value ofthe attribute information of the three-dimensional point after thechange. Additionally, when two or more three-dimensional points arechanged to the same three-dimensional position due to quantization orthe like, the three-dimensional data encoding device may assign theaverage value of the attribute information in the two or morethree-dimensional points before the change as the value of the attributeinformation after the change.

Next, the three-dimensional data encoding device encodes the attributeinformation (S6603). For example, when encoding a plurality of pieces ofattribute information, the three-dimensional data encoding device mayencode the plurality of pieces of attribute information in order. Forexample, when encoding the color and the reflectance as the attributeinformation, the three-dimensional data encoding device generates abitstream to which the encoding result of the reflectance is added afterthe encoding result of the color. Note that a plurality of encodingresults of the attribute information added to a bitstream may be in anyorder.

Additionally, the three-dimensional data encoding device may add theinformation indicating the start location of the encoded data of eachattribute information in a bitstream to a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount for thethree-dimensional data decoding device can be reduced. Additionally, thethree-dimensional data encoding device may encode a plurality of piecesof attribute information in parallel, and may integrate the encodingresults into one bitstream. Accordingly, the three-dimensional dataencoding device can encode a plurality of pieces of attributeinformation at high speed.

FIG. 35 is a flowchart of the attribute information encoding processing(S6603). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by the Haar conversion(S6611). Next, the three-dimensional data encoding device appliesquantization to the coding coefficient (S6612). Next, thethree-dimensional data encoding device generates encoded attributeinformation (bitstream) by encoding the coding coefficient after thequantization (S6613).

Additionally, the three-dimensional data encoding device applies inversequantization to the coding coefficient after the quantization (S6614).Next, the three-dimensional decoding device decodes the attributeinformation by applying the inverse Haar conversion to the codingcoefficient after the inverse quantization (S6615). For example, thedecoded attribute information is referred to in the following encoding.

FIG. 36 is a flowchart of the coding coefficient encoding processing(S6613). First, the three-dimensional data encoding device converts acoding coefficient from a signed integer value to an unsigned integervalue (S6621). For example, the three-dimensional data encoding deviceconverts a signed integer value to an unsigned integer value as follows.When signed integer value Ta1q is smaller than 0, the unsigned integervalue is set to −1−(2×Ta1q). When the signed integer value Ta1q is equalto or more than 0, the unsigned integer value is set to 2×Ta1q. Notethat, when the coding coefficient does not become a negative value, thethree-dimensional data encoding device may encode the coding coefficientas the unsigned integer value as is.

When not all coding coefficients have been processed (No in S6622), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (S6623). When the valueof the coding coefficient to be processed is zero (Yes in S6623), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6624),and returns to step S6622.

When the value of the coding coefficient to be processed is not zero (Noin S6623), the three-dimensional data encoding device encodes ZeroCnt,and resets ZeroCnt to zero (S6625). Additionally, the three-dimensionaldata encoding device arithmetically encodes the coding coefficient to beprocessed (S6626), and returns to step S6622. For example, thethree-dimensional data encoding device performs binary arithmeticencoding. In addition, the three-dimensional data encoding device maysubtract the value 1 from the coding coefficient, and may encode theobtained value.

Additionally, the processing of steps S6623 to S6626 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6622), the three-dimensionaldata encoding device ends the processing.

FIG. 37 is a flowchart of the three-dimensional data decoding processingaccording to the present embodiment. First, the three-dimensionaldecoding device decodes geometry information (geometry) from a bitstream(S6631). For example, the three-dimensional data decoding deviceperforms decoding by using an octree representation.

Next, the three-dimensional decoding device decodes the attributeinformation from the bitstream (S6632). For example, when decoding aplurality of pieces of attribute information, the three-dimensionaldecoding device may decode the plurality of pieces of attributeinformation in order. For example, when decoding the color and thereflectance as the attribute information, the three-dimensional datadecoding device decodes the encoding result of the color and theencoding result of the reflectance according to the order in which theyare added to the bitstream. For example, when the encoding result of thereflectance is added after the encoding result of the color in abitstream, the three-dimensional data decoding device decodes theencoding result of the color, and thereafter decodes the encoding resultof the reflectance. Note that the three-dimensional data decoding devicemay decode the encoding results of the attribute information added to abitstream in any order.

Additionally, the three-dimensional decoding device may obtain theinformation indicating the start location of the encoded data of eachattribute information in a bitstream by decoding a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount of thethree-dimensional decoding device can be reduced. Additionally, thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and may integrate the decodingresults into one three-dimensional point cloud. Accordingly, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at high speed.

FIG. 38 is a flowchart of the attribute information decoding processing(S6632). First, the three-dimensional decoding device decodes a codingcoefficient from a bitstream (S6641). Next, the three-dimensionaldecoding device applies the inverse quantization to the codingcoefficient (S6642). Next, the three-dimensional decoding device decodesthe attribute information by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization (S6643).

FIG. 39 is a flowchart of the coding coefficient decoding processing(S6641). First, the three-dimensional decoding device decodes ZeroCntfrom a bitstream (S6651). When not all coding coefficients have beenprocessed (No in S6652), the three-dimensional decoding devicedetermines whether ZeroCnt is larger than 0 (S6653).

When ZeroCnt is larger than zero (Yes in S6653), the three-dimensionaldecoding device sets the coding coefficient to be processed to 0(S6654). Next, the three-dimensional decoding device subtracts 1 fromZeroCnt (S6655), and returns to step S6652.

When ZeroCnt is zero (No in S6653), the three-dimensional decodingdevice decodes the coding coefficient to be processed (S6656). Forexample, the three-dimensional decoding device uses binary arithmeticdecoding. Additionally, the three-dimensional decoding device may addthe value 1 to the decoded coding coefficient.

Next, the three-dimensional decoding device decodes ZeroCnt, sets theobtained value to ZeroCnt (S6657), and returns to step S6652.

Additionally, the processing of steps S6653 to S6657 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6652), the three-dimensionaldata encoding device converts a plurality of decoded coding coefficientsfrom unsigned integer values to signed integer values (S6658). Forexample, the three-dimensional data decoding device may convert thedecoded coding coefficients from unsigned integer values to signedinteger values as follows. When the LSB (least significant bit) of thedecoded unsigned integer value Ta1u is 1, the signed integer value Ta1qis set to −((Ta1u+1)>>1). When the LSB of the decoded unsigned integervalue Ta1u is not 1 (when it is 0), the signed integer value Ta1q is setto (Ta1u>>1). Note that, when the coding coefficient does not become anegative value, the three-dimensional data decoding device may use thedecoded coding coefficient as is as the signed integer value.

FIG. 40 is a block diagram of attribute information encoder 6600included in the three-dimensional data encoding device. Attributeinformation encoder 6600 includes sorter 6601, Haar transformer 6602,quantizer 6603, inverse quantizer 6604, inverse Haar converter 6605,memory 6606, and arithmetic encoder 6607.

Sorter 6601 generates the Morton codes by using the geometry informationof three-dimensional points, and sorts the plurality ofthree-dimensional points in the order of the Morton codes. Haartransformer 6602 generates the coding coefficient by applying the Haarconversion to the attribute information. Quantizer 6603 quantizes thecoding coefficient of the attribute information.

Inverse quantizer 6604 inverse quantizes the coding coefficient afterthe quantization. Inverse Haar converter 6605 applies the inverse Haarconversion to the coding coefficient. Memory 6606 stores the values ofpieces of attribute information of a plurality of decodedthree-dimensional points. For example, the attribute information of thedecoded three-dimensional points stored in memory 6606 may be utilizedfor prediction and the like of an unencoded three-dimensional point.

Arithmetic encoder 6607 calculates ZeroCnt from the coding coefficientafter the quantization, and arithmetically encodes ZeroCnt.Additionally, arithmetic encoder 6607 arithmetically encodes thenon-zero coding coefficient after the quantization. Arithmetic encoder6607 may binarize the coding coefficient before the arithmetic encoding.In addition, arithmetic encoder 6607 may generate and encode variouskinds of header information.

FIG. 41 is a block diagram of attribute information decoder 6610included in the three-dimensional decoding device. Attribute informationdecoder 6610 includes arithmetic decoder 6611, inverse quantizer 6612,inverse Haar converter 6613, and memory 6614.

Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the codingcoefficient included in a bitstream. Note that arithmetic decoder 6611may decode various kinds of header information.

Inverse quantizer 6612 inverse quantizes the arithmetically decodedcoding coefficient. Inverse Haar converter 6613 applies the inverse Haarconversion to the coding coefficient after the inverse quantization.Memory 6614 stores the values of pieces of attribute information of aplurality of decoded three-dimensional points. For example, theattribute information of the decoded three-dimensional points stored inmemory 6614 may be utilized for prediction of an undcodedthree-dimensional point.

Note that, in the above-described embodiment, although the example hasbeen shown in which the three-dimensional points are encoded in theorder of the lower layers to the higher layers as the encoding order, itis not necessarily limit to this. For example, a method may be used thatscans the coding coefficients after the Haar conversion in the order ofthe higher layers to the lower layers. Note that, also in this case, thethree-dimensional data encoding device may encode the number ofconsecutive times of the value 0 as ZeroCnt.

Additionally, the three-dimensional data encoding device may switchwhether or not to use the encoding method using ZeroCnt described in thepresent embodiment per WLD, SPC, or volume. In this case, thethree-dimensional data encoding device may add the informationindicating whether or not the encoding method using ZeroCnt has beenapplied to the header information. Accordingly, the three-dimensionaldecoding device can appropriately perform decoding. As an example of theswitching method, for example, the three-dimensional data encodingdevice counts the number of times of occurrence of the codingcoefficient having a value of 0 with respect to one volume. When thecount value exceeds a predefined threshold value, the three-dimensionaldata encoding device applies the method using ZeroCnt to the nextvolume, and when the count value is equal to or less than the thresholdvalue, the three-dimensional data encoding device does not apply themethod using ZeroCnt to the next volume. Accordingly, since thethree-dimensional data encoding device can appropriately switch whetheror not to apply the encoding method using ZeroCnt according to thecharacteristic of a current three-dimensional point to be encoded, thecoding efficiency can be improved.

Hereinafter, another technique (modification) of the present embodimentwill be described. The three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) after thequantization according to a certain order. For example, thethree-dimensional data encoding device encodes a plurality ofthree-dimensional points from the three-dimensional points included inthe lower layers toward the higher layers in order.

FIG. 42 is a diagram showing an example of the first code sequence andthe second code sequence in the case where this technique is used forthe attribute information shown in FIG. 27. In the case of this example,the three-dimensional data encoding device encodes a plurality of codingcoefficients in the order of Ta5q, Tb1q, Tb3q, Tc1q, and d0q from Ta1qincluded in the lower layer L. Here, there is a tendency that the lowerthe layer, the more likely it is that the coding coefficient afterquantization becomes 0. This can be due to the following and the like.

Since the coding coefficients of the lower layers L show a higherfrequency component than the higher layers, the coding coefficients tendto be 0 depending on the current three-dimensional point to be encoded.Additionally, by switching the quantization scale according to theabove-described importance or the like. The lower the layer, the largerthe quantization scale, and the coding coefficient after thequantization easily become 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after the quantization becomes 0, and the value 0 islikely to be consecutively generated for the first code sequence. Thethree-dimensional data encoding device counts the number of times thatthe value 0 occurs in the first code sequence, and encodes the number oftimes (ZeroCnt) that the value 0 consecutively occurs, instead of theconsecutive values 0. Accordingly, when there are consecutive values 0of the coding coefficients after the quantization, the coding efficiencycan be improved by encoding the number of consecutive times of 0, ratherthan encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may encode theinformation indicating the total number of times of occurrence of thevalue 0. Accordingly, the overhead of encoding ZeroCnt can be reduced,and the coding efficiency can be improved.

For example, the three-dimensional data encoding device encodes thetotal number of the coding coefficients having a value of 0 asTotalZeroCnt. Accordingly, in the example shown in FIG. 42, at the timewhen the three-dimensional data decoding device decodes the secondZeroCnt (value 1) included in the second code sequence, the total numberof decoded ZeroCnts will be N+1 (=TotalZeroCnt). Therefore, thethree-dimensional data decoding device can identify that 0 does notoccur after this. Therefore, subsequently, it becomes unnecessary forthe three-dimensional data encoding device to encode ZeroCnt for eachvalue, and the code amount can be reduced.

Additionally, the three-dimensional data encoding device may entropyencode TotalZeroCnt. For example, the three-dimensional data encodingdevice binarizes the value of TotalZeroCnt with the truncated unary codeof the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after binarization. At this time, thethree-dimensional data encoding device may improve the coding efficiencyby using a different coding table for each bit. For example, thethree-dimensional data encoding device uses coding table 1 for the firstbit, uses coding table 2 for the second bit, and coding table 3 for thesubsequent bits. In this manner, the three-dimensional data encodingdevice can improve the coding efficiency by switching the coding tablefor each bit.

Additionally, the three-dimensional data encoding device mayarithmetically encode TotalZeroCnt after binarizing TotalZeroCnt with anExponential-Golomb. Accordingly, when the value of TotalZeroCnt easilybecomes large, the efficiency can be more improved than the binarizedarithmetic encoding with the truncated unary code. Note that thethree-dimensional data encoding device may add a flag for switchingbetween using the truncated unary code and using the Exponential-Golombto a header. Accordingly, the three-dimensional data encoding device canimprove the coding efficiency by selecting the optimum binarizationmethod. Additionally, the three-dimensional data decoding device cancorrectly decode a bitstream by referring to the flag included in theheader to switch the binarization method.

FIG. 43 is a diagram showing a syntax example of the attributeinformation (attribute_data) in the present modification. The attributeinformation (attribute_data) shown in FIG. 43 further includes the totalnumber of zeros (TotalZeroCnt) in addition to the attribute informationshown in FIG. 32. Note that the other information is the same as that inFIG. 32. The total number of zeros (TotalZeroCnt) indicates the totalnumber of the coding coefficients having a value of 0 afterquantization.

Additionally, the three-dimensional data encoding device may switch thecalculation method of the values of TotalZeroCnt and ZeroCnt dependingon the value of attribute_dimension. For example, whenattribute_dimension=3, the three-dimensional data encoding device maycount the number of times that the values of the coding coefficients ofall the components (dimensions) become 0. FIG. 44 is a diagram showingan example of the coding coefficient, ZeroCnt, and TotalZeroCnt in thiscase. For example, in the case of the color information shown in FIG.44, the three-dimensional data encoding device counts the number of theconsecutive coding coefficients having 0 for all of the R, G, and Bcomponents, and adds the counted number to a bitstream as TotalZeroCntand ZeroCnt. Accordingly, it becomes unnecessary to encode Total ZeroCntand ZeroCnt for each component, and the overhead can be reduced.Therefore, the coding efficiency can be improved. Note that thethree-dimensional data encoding device may calculate TotalZeroCnt andZeroCnt for each dimension even when attribute_dimension is two or more,and may add the calculated TotalZeroCnt and ZeroCnt to a bitstream.

FIG. 45 is a flowchart of the coding coefficient encoding processing(S6613) in the present modification. First, the three-dimensional dataencoding device converts the coding coefficient from a signed integervalue to an unsigned integer value (S6661). Next, the three-dimensionaldata encoding device encodes TotalZeroCnt (S6662).

When not all coding coefficients have been processed (No in S6663), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (S6664). When the valueof the coding coefficient to be processed is zero (Yes in S6664), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6665),and returns to step S6663.

When the value of the coding coefficient to be processed is not zero (Noin S6664), the three-dimensional data encoding device determines whetherTotalZeroCnt is larger than 0 (S6666). When TotalZeroCnt is larger than0 (Yes in S6666), the three-dimensional data encoding device encodesZeroCnt, and sets TotalZeroCnt to TotalZeroCnt−ZeroCnt (S6667).

After step S6667, or when TotalZeroCnt is 0 (No in S6666), thethree-dimensional data encoding device encodes the coding coefficient,resets ZeroCnt to 0 (S6668), and returns to step S6663. For example, thethree-dimensional data encoding device performs binary arithmeticencoding. Additionally, the three-dimensional data encoding device maysubtract the value 1 from the coding coefficient, and encode theobtained value.

Additionally, the processing of steps S6664 to S6668 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6663), the three-dimensionaldata encoding device ends processing.

FIG. 46 is a flowchart of the coding coefficient decoding processing(S6641) in the present modification. First, the three-dimensionaldecoding device decodes TotalZeroCnt from a bitstream (S6671). Next, thethree-dimensional decoding device decodes ZeroCnt from the bitstream,and sets TotalZeroCnt to TotalZeroCnt−ZeroCnt (S6672).

When not all coding coefficients have been processed (No in S6673), thethree-dimensional data encoding device determines whether ZeroCnt islarger than 0 (S6674).

When ZeroCnt is larger than zero (Yes in S6674), the three-dimensionaldata decoding device sets the coding coefficient to be processed to 0(S6675). Next, the three-dimensional data decoding device subtracts 1from ZeroCnt (S6676), and returns to step S6673.

When ZeroCnt is zero (No in S6674), the three-dimensional data decodingdevice decodes the coding coefficient to be processed (S6677). Forexample, the three-dimensional data decoding device uses binaryarithmetic decoding. Additionally, the three-dimensional data decodingdevice may add the value 1 to the decoded coding coefficient.

Next, the three-dimensional data decoding device determines whetherTotalZeroCnt is larger than 0 (S6678). When TotalZeroCnt is larger than0 (Yes in S6678), the three-dimensional data decoding device decodesZeroCnt, sets the obtained value to ZeroCnt, sets TotalZeroCnt toTotalZeroCnt−ZeroCnt (S6679), and returns to step S6673. Additionally,when TotalZeroCnt is 0 (No in S6678), the three-dimensional decodingdevice returns to step S6673.

Additionally, the processing of steps S6674 to S6679 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6673), the three-dimensionaldata encoding device converts the decoded coding coefficient from anunsigned integer value to a signed integer value (S6680).

FIG. 47 is a diagram showing another syntax example of the attributeinformation (attribute_data). The attribute information (attribute_data)shown in FIG. 47 includes value [j] [i]_greater_zero_flag, value [j][i]_greater_one_flag, and value [j] [i], instead of the codingcoefficient (value [j] [i]) shown in FIG. 32. Note that the otherinformation is the same as that in FIG. 32.

Value [j] [i]_greater_zero_flag indicates whether or not the value ofthe coding coefficient (value [j] [i]) is larger than 0. In other words,value [j] [i]_greater_zero_flag indicates whether or not the value ofthe coding coefficient (value [j] [i]) is 0.

For example, when the value of the coding coefficient is larger than 0,value [j] [i]_greater_zero_flag is set to the value 1, and when thevalue of the coding coefficient is 0, value [j] [i]_greater_zero_flag isset to the value 0. When the value of value [j] [i]_greater_zero_flag is0, the three-dimensional data encoding device need not add value [j] [i]to a bitstream. In this case, the three-dimensional decoding device maydetermine that the value of value [j] [i] is the value 0. Accordingly,the code amount can be reduced.

Value [j] [i]_greater_one_flag indicates whether or not the value of thecoding coefficient (value [j] [i]) is larger than 1 (is equal to orlarger than 2). In other words, value [j] [i]_greater_one_flag indicateswhether or not the value of the coding coefficient (value [j] RD is 1.

For example, when the value of the coding coefficient is larger than 1,value [j] [i]_greater_one_flag is set to the value 1. Otherwise (whenthe value of the coding coefficient is equal to or less than 1), value[j] [i]_greater_one_flag is set to the value 0. When the value of value[j] [i]_greater_one_flag is 0, the three-dimensional data encodingdevice need not add value [j] [i] to a bitstream. In this case, thethree-dimensional decoding device may determine that the value of value[j] [i] is the value 1.

Value [j] [i] indicates the coding coefficient after quantization of theattribute information of the j-th dimension of the i-ththree-dimensional point. For example, when the attribute information iscolor information, value [99] [1] indicates the coding coefficient ofthe second dimension (for example, the G value) of the 100ththree-dimensional point. Additionally, when the attribute information isreflectance information, value [119] [0] indicates the codingcoefficient of the first dimension (for example, the reflectance) of the120th three-dimensional point.

When value [j] [i]_greater_zero_flag=1, and value [j][i]_greater_one_flag=1, the three-dimensional data encoding device mayadd value [j] [i] to a bitstream. Additionally, the three-dimensionaldata encoding device may add the value obtained by subtracting 2 fromvalue [j] [i] to the bitstream. In this case, the three-dimensionaldecoding device calculates the coding coefficient by adding the value 2to the decoded value [j] [i].

The three-dimensional data encoding device may entropy encode value [j][i]_greater_zero_flag and value [j] [i]_greater_one_flag. For example,binary arithmetic encoding and binary arithmetic decoding may be used.Accordingly, the coding efficiency can be improved.

Embodiment 8

To achieve high compression, attribute information included in PointCloud Compression (PCC) data is transformed in a plurality of methods,such as Lifting, Region Adaptive Hierarchical Transform (RAHT) and othertransformation methods. Here, Lifting is one of transformation methodsusing Level of Detail (LoD).

Important signal information tends to be included in a low frequencycomponent, and therefore the code amount is reduced by quantizing a highfrequency component. That is, the transformation process has strongenergy compression characteristics. In addition, the precision isreduced by the quantization according to the magnitude of thequantization parameter.

FIG. 48 is a block diagram showing a configuration of athree-dimensional data encoding device according to this embodiment. Thethree-dimensional data encoding device includes subtractor 7001,transformer 7002, transformation matrix holder 7003, quantizer 7004,quantization controller 7005, and entropy encoder 7006.

Subtractor 7001 calculates a coefficient value that is the differencebetween input data and reference data. For example, the input data isattribute information included in point cloud data, and the referencedata is a predicted value of the attribute information.

Transformer 7002 performs a transformation process on the coefficientvalue. For example, the transformation process is a process ofclassifying a plurality of pieces of attribute information into LoDs.Note that the transformation process may be Haar transformation or thelike. Transformation matrix holder 7003 holds a transformation matrixused for the transformation process by transformer 7002. For example,the transformation matrix is a Haar transformation matrix. Note thatalthough an example is shown here in which the three-dimensional dataencoding device has both a function of performing a transformationprocess using LoDs and a function of performing a transformation processsuch as Haar transformation, the three-dimensional data encoding devicemay have only any one of the functions. Alternatively, thethree-dimensional data encoding device may selectively use any of thesetwo kinds of transformation processes. Alternatively, thethree-dimensional data encoding device may change the transformationprocess to be used for each predetermined processing unit.

Quantizer 7004 quantizes the coefficient value to generate a quantizedvalue. Quantization controller 7005 controls a quantization parameterused for the quantization by quantizer 7004. For example, quantizationcontroller 7005 may change the quantization parameter (or quantizationstep) according to the hierarchical structure for the encoding. In thisway, an appropriate quantization parameter can be selected for eachlayer of the hierarchical structure, so that the amount of codesoccurring in each layer can be controlled. Quantization controller 7005also sets quantization parameters for a certain layer and the layerslower than the certain layer that include a frequency component that hasa small effect on the subjective image quality at a maximum value, andsets quantization coefficients for the certain layer and the layerslower than the certain layer at 0, for example. In this way, theoccurring code amount can be reduced while reducing the deterioration ofthe subjective image quality. Quantization controller 7005 can alsofinely control the subjective image quality and the occurring codeamount. The “layer” herein refers to a layer (at a depth in a treestructure) in LoD or RAHT (Haar transformation).

Entropy encoder 7006 entropy-encodes (arithmetically encodes, forexample) the quantization coefficient to generate a bitstream. Entropyencoder 7006 also encodes the quantization parameter for each layer setby quantization controller 7005.

FIG. 49 is a block diagram showing a configuration of athree-dimensional data decoding device according to this embodiment. Thethree-dimensional data decoding device includes entropy decoder 7011,inverse quantizer 7012, quantization controller 7013, inversetransformer 7014, transformation matrix holder 7015, and adder 7016.

Entropy decoder 7011 decodes the quantization coefficient and thequantization parameter for each layer from the bitstream. Inversequantizer 7012 inverse-quantizes the quantization coefficient togenerate the coefficient value. Quantization controller 7013 controlsthe quantization parameter used for the inverse quantization by inversequantizer 7012 based on the quantization parameter for each layerobtained in entropy decoder 7011.

Inverse transformer 7014 inverse-transforms the coefficient value. Forexample, inverse transformer 7014 performs inverse Haar transformationon the coefficient value. Transformation matrix holder 7015 holds atransformation matrix used for the inverse transformation process byinverse transformer 7014. For example, the transformation matrix isinverse Haar transformation matrix.

Adder 7016 adds the reference data to the coefficient value to generateoutput data. For example, the output data is attribute informationincluded in point cloud data, and the reference data is a predictedvalue of the attribute information.

Next, the setting of a quantization parameter for each layer will bedescribed. In the encoding of attribute information, such asPredicting/Lifting, a different quantization parameter is used for eachLoD layer. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each LoD, according to the use plan ofthe user. Here, the quantization tree value is the quantizationparameter, for example.

FIG. 50 is a diagram showing an example of the setting of LoDs. As shownin FIG. 50, for example, independent Qt0 to Qt2 are set for LoD0 toLoD2.

In the encoding of the attribute information using RAHT, differentquantization parameters are used according to the depth in the treestructure. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each depth in the tree structure,according to the use plan of the user.

FIG. 51 is a diagram showing an example of a hierarchical structure(tree structure) of RAHT. As shown in FIG. 51, for example, independentQt0 to Qt2 are set for depths in the tree structure.

In the following, a configuration of a three-dimensional data encodingdevice according to this embodiment will be described. FIG. 52 is ablock diagram showing a configuration of three-dimensional data encodingdevice 7020 according to this embodiment. Three-dimensional dataencoding device 7020 encodes point cloud data (point cloud) to generateencoded data (encoded stream). Three-dimensional data encoding device7020 includes divider 7021, a plurality of geometry information encoders7022, a plurality of attribute information encoders 7023, additionalinformation encoder 7024, and multiplexer 7025.

Divider 7021 generates a plurality of pieces of divisional data bydividing point cloud data. Specifically, divider 7021 generates aplurality of pieces of divisional data by dividing a space of pointcloud data into a plurality of subspaces. Here, a subspace is acombination of tiles or slices or a combination of tiles and slices.More specifically, point cloud data includes geometry information,attribute information (such as color or reflectance), and additionalinformation. Divider 7021 divides geometry information into a pluralityof pieces of divisional geometry information, and divides attributeinformation into a plurality of pieces of divisional attributeinformation. Divider 7021 also generates additional informationconcerning the division.

Divider 7021 first divides a point cloud into tiles, for example.Divider 7021 then further divides the resulting tiles into slices.

The plurality of geometry information encoders 7022 generate a pluralityof pieces of encoded geometry information by encoding a plurality ofpieces of divisional geometry information. For example, geometryinformation encoders 7022 encode divisional geometry information usingan N-ary tree structure, such as an octree. Specifically, in the case ofan octree, a current space is divided into eight nodes (subspaces), and8-bit information (occupancy code) that indicates whether each nodeincludes a point cloud or not is generated. A node including a pointcloud is further divided into eight nodes, and 8-bit information thatindicates whether each of the eight nodes includes a point cloud or notis generated. This process is repeated until a predetermined layer isreached or the number of the point clouds included in each node becomesequal to or less than a threshold. For example, the plurality ofgeometry information encoders 7022 process a plurality of pieces ofdivisional geometry information in parallel.

Attribute information encoder 7023 generates encoded attributeinformation, which is encoded data, by encoding attribute informationusing configuration information generated by geometry informationencoder 7022. For example, attribute information encoder 7023 determinesa reference point (reference node) that is to be referred to in encodinga current point (current node) to be processed based on the octreestructure generated by geometry information encoder 7022. For example,attribute information encoder 7023 refers to a node whose parent node inthe octree is the same as the parent node of the current node, ofperipheral nodes or neighboring nodes. Note that the method ofdetermining a reference relationship is not limited to this method. Theprocess of encoding geometry information or attribute information mayinclude at least one of a quantization process, a prediction process,and an arithmetic encoding process. In this case, “refer to” means usinga reference node for calculation of a predicted value of attributeinformation or using a state of a reference node (occupancy informationthat indicates whether a reference node includes a point cloud or not,for example) for determination of a parameter of encoding. For example,the parameter of encoding is a quantization parameter in thequantization process or a context or the like in the arithmeticencoding.

Additional information encoder 7024 generates encoded additionalinformation by encoding the additional information included in the pointcloud data and the additional information concerning the data divisiongenerated in the division by divider 7021.

Multiplexer 7025 generates encoded stream (encoded stream) bymultiplexing the plurality of pieces of encoded geometry information,the plurality of pieces of encoded attribute information, and theencoded additional information, and transmits the generated encodeddata. The encoded additional information is used in the decoding.

FIG. 53 is a block diagram of divider 7021. Divider 7021 includes tiledivider 7031 and slice divider 7032.

Tile divider 7031 generates a plurality of pieces of tile geometryinformation by dividing geometry information (position (geometry)) intotiles. Tile divider 7031 generates a plurality of pieces of tileattribute information by dividing attribute information (attribute) intotiles. Tile divider 7031 also outputs tile additional information (TileMetaData) including information concerning the tile division andinformation generated in the tile division.

Slice divider 7032 generates a plurality of pieces of divisionalgeometry information (a plurality of pieces of slice geometryinformation) by dividing a plurality of pieces of tile geometryinformation into slices. Slice divider 7032 generates a plurality ofpieces of divisional attribute information (a plurality of pieces ofslice attribute information) by dividing a plurality of pieces of tileattribute information into slices. Slice divider 7032 also outputs sliceadditional information (Slice MetaData) including information concerningthe slice division and information generated in the slice division.

Tile divider 7031 and slice divider 7032 also determine a quantizationtree value (quantization parameter) based on the generated additionalinformation.

FIG. 54 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes transformer 7035, quantizer7036, and entropy encoder 7037.

Transformer 7035 classifies the divisional attribute information intolayers, such as LoDs, and generates a coefficient value (differencevalue) by calculating the difference between the divisional attributeinformation and the predicted value. Note that transformer 7035 maygenerate the coefficient value by performing the Haar transformation onthe divisional attribute information.

Quantizer 7036 generates a quantized value by quantizing the coefficientvalue. Specifically, quantizer 7036 divides the coefficient by aquantization step based on the quantization parameter. Entropy encoder7037 generates encoded attribute information by entropy-encoding thequantized value.

In the following, a configuration of a three-dimensional data decodingdevice according to this embodiment will be described. FIG. 55 is ablock diagram showing a configuration of three-dimensional data decodingdevice 7040. Three-dimensional data decoding device 7040 reproducespoint cloud data by decoding encoded data (encoded stream) generated byencoding the point cloud data. Three-dimensional data decoding device7040 includes demultiplexer 7041, a plurality of geometry informationdecoders 7042, a plurality of attribute information decoders 7043,additional information decoder 7044, and combiner 7045.

Demultiplexer 7041 generates a plurality of pieces of encoded geometryinformation, a plurality of pieces of encoded attribute information, andencoded additional information by demultiplexing encoded data (encodedstream).

The plurality of geometry information decoders 7042 generates aplurality of pieces of divisional geometry information by decoding aplurality of pieces of encoded geometry information. For example, theplurality of geometry information decoders 7042 process a plurality ofpieces of encoded geometry information in parallel.

The plurality of attribute information decoders 7043 generate aplurality of pieces of divisional attribute information by decoding aplurality of pieces of encoded attribute information. For example, theplurality of attribute information decoders 7043 process a plurality ofpieces of encoded attribute information in parallel.

A plurality of additional information decoders 7044 generate additionalinformation by decoding encoded additional information.

Combiner 7045 generates geometry information by combining a plurality ofpieces of divisional geometry information using additional information.Combiner 7045 generates attribute information by combining a pluralityof pieces of divisional attribute information using additionalinformation.

FIG. 56 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes entropy decoder 7051,inverse quantizer 7052, and inverse transformer 7053. Entropy decoder7051 generates a quantized value by entropy-decoding encoded attributeinformation. Inverse quantizer 7052 generates a coefficient value byinverse-quantizing the quantized value. Specifically, inverse quantizer7052 multiplies the coefficient value by a quantization step based onthe quantization tree value (quantization parameter) obtained from thebitstream. Inverse transformer 7053 generates divisional attributeinformation by inverse-transforming the coefficient value. Here, theinverse transformation is a process of adding the predicted value to thecoefficient value, for example. Alternatively, the inversetransformation is the inverse Haar transformation.

In the following, an example of a method of determining a quantizationparameter will be described. FIG. 57 is a diagram showing an example ofthe setting of a quantization parameter in the tile division and theslice division.

When the value of the quantization parameter is small, the originalinformation is likely to be maintained. For example, a default value ofthe quantization parameter is 1. For example, in the encoding processusing tiles of PCC data, a quantization parameter for a tile of a mainroad is set to be a small value, in order to maintain the data quality.On the other hand, a quantization parameter for a tile of a peripheralarea is set to be a great value. In this way, the coding efficiency canbe improved, while the data quality of the peripheral area decreases.

Similarly, in the encoding process using slices of PCC data, a sidewalk,a tree, and a building are important in the self-position estimation andmapping, and a quantization parameter for a slice of a sidewalk, a tree,or a building is set to be a small value. On the other hand, a movingbody or other objects is less important, so that a quantizationparameter for a slice of a moving body or other objects is set to be agreat value.

When ΔQP (DeltaQP) described later is used, in the encoding of athree-dimensional point belonging to an important area, such as a mainroad, the three-dimensional data encoding device may perform theencoding by setting the value of ΔQP to be a negative value to reducethe quantization error, in order to decrease the quantization parameter.In this way, the decoded attribute value of the three-dimensional pointbelonging to the important area can be brought close to the value beforethe encoding. In the encoding of a three-dimensional point belonging toan area that is not important, such as a peripheral area, thethree-dimensional data encoding device may set the value of ΔQP to be apositive value to reduce the information amount, in order to increasethe quantization parameter. In this way, the total code amount can bereduced, while maintaining the amount of information on the importantarea.

In the following, an example of information that indicates aquantization parameter for each layer will be described. In encodingattribute information on a three-dimensional point by quantization, ascheme for controlling quantization parameters on a finer unit basis isintroduced in addition to quantization parameter QPbase for a frame, aslice, a tile, or the like. For example, when encoding attributeinformation using LoDs, the three-dimensional data encoding deviceperform the encoding by changing the value of the quantization parameterfor each LoD by providing Delta_Layer for each LoD and addingDelta_Layer to the value of QPbase for each LoD. The three-dimensionaldata encoding device also adds Delta_Layer used for the encoding to aheader or the like of the bitstream. In this way, the three-dimensionaldata encoding device can encode attribute information on athree-dimensional point by changing the quantization parameter for eachLoD according to a desired code amount and an actual code amount, forexample, and therefore can finally generate a bitstream having a codeamount close to the desired code amount. The three-dimensional datadecoding device can properly decode the bitstream by decoding QPbase andDelta_Layer included in the header to generate the quantizationparameters used by the three-dimensional data encoding device.

FIG. 58 is a diagram showing an example in which attribute informationon all three-dimensional points are encoded using quantization parameterQPbase. FIG. 59 is a diagram showing an example in which encoding isperformed by changing the quantization parameter for each LoD layer. Inthe example shown in FIG. 59, the quantization parameter for the leadingLoD is calculated by adding Delta_Layer of the leading LoD to QPbase.For the second and following LoDs, the quantization parameter for theLoD being processed is calculated by adding Delta_Layer of the LoD beingprocessed to the quantization parameter for the immediately precedingLoD. For example, quantization parameter QP3 of at the head of LoD3 iscalculated according to QP3=QP2+Delta_Layer[3].

Note that Delta_Layer[i] for each LoD may indicate the difference valuewith respect to QPbase. That is, quantization parameter QPi of i-th LoDiis indicated by QPi=QPbase+Delta_Layer[i]. For example,QP1=QPbase+Delta_Layer[1], and QP2=QPbase+Delta_Layer[2].

FIG. 60 is a diagram showing a syntax example of an attributeinformation header (attribute header information). Here, the attributeinformation header is a header on a frame, slice or tile basis, forexample, and is a header of attribute information. As shown in FIG. 60,the attribute information header includes QPbase (reference quantizationparameter), NumLayer (number of layers), and Delta_Layer[i](differential quantization parameter).

QPbase indicates the value of a reference quantization parameter for aframe, a slice, a tile, or the like. NumLayer indicates the number oflayers of LoD or RAHT. In other words, NumLayer indicates the number ofall Delta_Layer[i] included in the attribute information header.

Delta_Layer[i] indicates the value of ΔQP for layer i. Here, ΔQP is avalue obtained by subtracting the quantization parameter for layer ifrom the quantization parameter for layer i−1. Note that ΔQP may be avalue obtained by subtracting the quantization parameter for layer ifrom QPbase. ΔQP can assume a positive or negative value. Note thatDelta_Layer[0] need not be added to the header. In that case, thequantization parameter for layer 0 is equal to QPbase. In this way, thecode amount of the header can be reduced.

FIG. 61 is a diagram showing another syntax example of an attributeinformation header (attribute header information). The attributeinformation header shown in FIG. 61 differs from the attributeinformation header shown in FIG. 60 in that the attribute informationheader further includes delta_Layer_present_flag.

delta_Layer_present_flag is a flag that indicates whether Delta_Layer isincluded in the bitstream or not. For example, a value of 1 indicatesthat Delta_Layer is included in the bitstream, and a value of 0indicates that Delta_Layer is not included in the bitstream. Whendelta_Layer_present_flag is 0, the three-dimensional data decodingdevice performs the following decoding process by setting Delta_Layer tobe 0, for example.

Note that although examples have been described here in which thequantization parameter is indicated by QPbase and Delta_Layer, aquantization step may be indicated by QPbase and Delta_Layer. Thequantization step is calculated from the quantization parameter using aformula, a table or the like determined in advance. In the quantizationprocess, the three-dimensional data encoding device divides thecoefficient value by the quantization step. In the inverse quantizationprocess, the three-dimensional data decoding device reproduces thecoefficient value by multiplying the quantized value by the quantizationstep.

Next, an example in which the quantization parameters are controlled ona finer unit basis will be described. FIG. 62 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a unit finer than LoD.

For example, when encoding attribute information using LoD, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each LoD layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on a plurality of three-dimensional pointsby changing the quantization parameter for each three-dimensional pointaccording to the desired code amount and the actual code amount, forexample. In this way, the three-dimensional data encoding device canfinally generate a bitstream having a code amount close to the desiredcode amount. The three-dimensional data decoding device can properlydecode the bitstream by decoding QPbase, Delta_Layer, and ADeltaincluded in the header to generate the quantization parameters used bythe three-dimensional data encoding device.

For example, as shown in FIG. 62, quantized value QP4 of NO-th attributeinformation is calculated according to QP4=QP3+ADelta_QP[0].

An encoding/decoding order reverse to the encoding/decoding order shownin FIG. 62 can also be used. For example, encoding/decoding can also beperformed in the order of LoD3, LoD2, LoD1, and then LoD0.

FIG. 63 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 62 is used. The attribute information header shownin FIG. 63 differs from the attribute information header shown in FIG.60 in that the attribute information header further includes NumADelta,NumPointADelta[i], and ADelta_QP[i].

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the LoD to which three-dimensionalpoint A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

ADelta_QP[i] indicates the value of ΔQP of the three-dimensional pointindicated by NumPointADelta[i]. That is, ADelta_QP[i] indicates thedifference between the quantization parameter of the three-dimensionalpoint indicated by NumPointADelta[i] and the quantization parameter ofthe three-dimensional point immediately preceding that three-dimensionalpoint.

FIG. 64 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 62 is used. The attribute information header shownin FIG. 64 differs from the attribute information header shown in FIG.63 in that the attribute information header further includesdelta_Layer_present_flag and additional_delta_QP_present_flag andincludes NumADelta_minus1 instead of NumADelta.

delta_Layer_present_flag is the same as that already described withreference to FIG. 61.

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus 1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 65 is a flowchart of a three-dimensional data encoding processaccording to this embodiment. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7001). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7002). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7003). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 66 is a flowchart of the attribute information encoding process(S7003). First, the three-dimensional data encoding device sets an LoD(S7011). That is, the three-dimensional data encoding device assignseach three-dimensional point to any of a plurality of LoDs.

The three-dimensional data encoding device then starts a loop on an LoDbasis (S7012). That is, the three-dimensional data encoding devicerepeatedly performs the process from step S7013 to step S7021 for eachLoD.

The three-dimensional data encoding device then starts a loop on a basisof a three-dimensional point (S7013). That is, the three-dimensionaldata encoding device repeatedly performs the process from step S7014 tostep S7020 for each three-dimensional point.

First, the three-dimensional data encoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7014). The three-dimensionaldata encoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7015). The three-dimensionaldata encoding device then calculates a prediction residual, which is thedifference between the attribute information and the predicted value ofthe current three-dimensional point (S7016). The three-dimensional dataencoding device then calculates a quantized value by quantizing theprediction residual (S7017). The three-dimensional data encoding devicethen arithmetically encodes the quantized value (S7018). Thethree-dimensional data encoding device then determines ΔQP (S7019). ΔQPdetermined here is used for determining a quantization parameter usedfor quantization of a subsequent prediction residual.

The three-dimensional data encoding device then calculates aninverse-quantized value by inverse-quantizing the quantized value(S7020). The three-dimensional data encoding device then generates adecoded value by adding the predicted value to the inverse-quantizedvalue (S7021). The three-dimensional data encoding device then ends theloop on a basis of a three-dimensional point (S7022). Thethree-dimensional data encoding device also ends the loop on a LoD basis(S7023).

FIG. 67 is a flowchart of the ΔQP determination process (S7019). First,the three-dimensional data encoding device calculates layer i to whichcurrent three-dimensional point A to be encoded next belongs andencoding order N (S7031). Layer i indicates a LoD layer or a RAHT layer,for example. The three-dimensional data encoding device then adds theactual code amount to a cumulative code amount (S7032). Here, thecumulative code amount refers to a cumulative code amount for one frame,one slice, or one tile of the current three-dimensional point. Note thatthe cumulative code amount may refer to a cumulative code amount for aplurality of frames, a plurality of slices, or a plurality of tiles.Alternatively, a cumulative code amount of attribute information may beused, or a cumulative code amount of both geometry information andattribute information may be used.

The three-dimensional data encoding device then determines whether thecumulative code amount is greater than the desired code amount×TH1 ornot (S7033). Here, the desired code amount refers to a desired codeamount for one frame, one slice, or one tile of the currentthree-dimensional point. Note that the desired code amount may refer toa desired code amount for a plurality of frames, a plurality of slices,or a plurality of tiles. Alternatively, a desired code amount ofattribute information may be used, or a desired code amount of bothgeometry information and attribute information may be used.

When the cumulative code amount is equal to or smaller than the desiredcode amount×TH1 (if No in S7033), the three-dimensional data encodingdevice determines whether the cumulative code amount is greater than thedesired code amount×TH2 or not (S7036).

Here, as thresholds TH1 and TH2, values from 0.0 to 1.0 are set, forexample. In addition, TH1>TH2. For example, when the cumulative codeamount is greater than the value of the desired code amount×TH1 (if Yesin S7033), the three-dimensional data encoding device determines thatthe code amount needs to be reduced as early as possible, and setsADelta_QP to value a in order to increase the quantization parameter fornext three-dimensional point N. The three-dimensional data encodingdevice also sets NumPointADelta to value N, and increment j by 1(S7034). The three-dimensional data encoding device then addsADelta_QP=a and NumPointADelta=N to the header (S7035). Note that valuea may be a fixed value or a variable value. For example, thethree-dimensional data encoding device may determine value a based onthe magnitude of the difference between the cumulative code amount andthe desired code amount×TH1. For example, the three-dimensional dataencoding device sets value a to be greater as the difference between thecumulative code amount and the desired code amount×TH1 increases. Inthis way, the three-dimensional data encoding device can control thequantization parameter so that the cumulative code amount does notexceed the desired code amount.

When the cumulative code amount is greater than the desired codeamount×TH2 (if Yes in S7036), the three-dimensional data encoding devicesets Delta_Layer to value β in order to increase the quantizationparameter for layer i to which current three-dimensional point A belongsor the subsequent layer i+1 (S7037). For example, the three-dimensionaldata encoding device sets Delta_Layer[i] of layer i to be value β whencurrent three-dimensional point A is at the top of layer i, and setsDelta_Layer[i+1] of layer i+1 to be value β when currentthree-dimensional point A is not at the top of layer i.

The three-dimensional data encoding device adds Delta_Layer=13 of layeri or layer i+1 to the header (S7038). Note that value β may be a fixedvalue or a variable value. For example, the three-dimensional dataencoding device may determine value β based on the magnitude of thedifference between the cumulative code amount and the desired codeamount×TH2. For example, the three-dimensional data encoding device setsvalue β to be greater as the difference between the cumulative codeamount and the desired code amount×TH2 increases. In this way, thethree-dimensional data encoding device can control the quantizationparameter so that the cumulative code amount does not exceed the desiredcode amount.

If the cumulative code amount exceeds or is about to exceed the desiredcode amount, the three-dimensional data encoding device may set thevalue of ADelta_QP or Delta_Layer so that the quantization parameterassumes the maximum value supported by the standard or the like. In thisway, the three-dimensional data encoding device can set the quantizationcoefficient for points subsequent to three-dimensional point A or layerssubsequent to layer i to be 0, thereby reducing the increase of theactual code amount and preventing the cumulative code amount fromexceeding the desired code amount. If the cumulative code amount issmaller than the desired code amount×TH3, the three-dimensional dataencoding device may decrease the quantization parameter so that theactual code amount increases. For example, the three-dimensional dataencoding device may decrease the quantization parameter by setting thevalue of Delta_Layer or ADelta_QP to be a negative value depending onthe difference between the cumulative code amount and the desired codeamount. In this way, the three-dimensional data encoding device cangenerate a bitstream having a code amount close to the desired codeamount.

FIG. 68 is a flowchart of a three-dimensional data decoding processaccording to this embodiment. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7005). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7006). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 69 is a flowchart of the attribute information decoding process(S7006). First, the three-dimensional data decoding device sets an LoD(S7041). That is, the three-dimensional data decoding device assignseach of a plurality of three-dimensional points having decoded geometryinformation to any of a plurality of LoDs. For example, the method ofthe assignment is the same as the method of assignment used in thethree-dimensional data encoding device.

The three-dimensional data decoding device then decodes ΔQP from thebitstream (S7042). Specifically, the three-dimensional data decodingdevice decodes Delta_Layer, ADelta_QP, and NumPointADelta from theheader of the bitstream.

The three-dimensional data decoding device then starts a loop on an LoDbasis (S7043). That is, the three-dimensional data decoding devicerepeatedly performs the process from step S7044 to step S7050 for eachLoD. The three-dimensional data decoding device then starts a loop on abasis of a three-dimensional point (S7044). That is, thethree-dimensional data decoding device repeatedly performs the processfrom step S7045 to step S7049 for each three-dimensional point.

First, the three-dimensional data decoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7045). The three-dimensionaldata decoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7046). Note that theseprocessings are the same as those in the three-dimensional data encodingdevice.

The three-dimensional data decoding device then arithmetically decodesthe quantized value from the bitstream (S7047). The three-dimensionaldata decoding device then calculates an inverse-quantized value byinverse-quantizing the decoded quantized value (S7048). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7042 is used.

The three-dimensional data decoding device then generates a decodedvalue by adding the predicted value to the inverse-quantized value(S7049). The three-dimensional data decoding device then ends the loopon a basis of a three-dimensional point (S7050). The three-dimensionaldata decoding device also ends the loop on a LoD basis (S7051).

FIG. 70 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes LoD setter 7061, searcher7062, predictor 7063, subtractor 7064, quantizer 7065, inverse quantizer7066, reconstructor 7067, memory 7068, and ΔQP calculator 7070.

LoD setter 7061 generates a LoD using geometry information on athree-dimensional point. Searcher 7062 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints. Predictor 7063 generates a predicted value of attributeinformation of a current three-dimensional point. Predictor 7063 alsoassigns a predicted value to a plurality of prediction modes 0 to M−1,and selects a prediction mode to be used from the plurality ofprediction modes.

Subtractor 7064 generates a prediction residual by subtracting thepredicted value from the attribute information. Quantizer 7065 quantizesthe prediction residual of the attribute information. Inverse quantizer7066 inverse-quantizes the quantized prediction residual. Reconstructor7067 generates a decoded value by summing the predicted value and theinverse-quantized prediction residual. Memory 7068 stores the value(decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7068 is used for prediction ofa three-dimensional point yet to be encoded by predictor 7063.

Arithmetic encoder 7069 calculates ZeroCnt from the quantized predictionresidual, and arithmetically encodes ZeroCnt. Arithmetic encoder 7069also arithmetically encodes any quantized prediction residual that isnot zero. Arithmetic encoder 7069 may binarize the prediction residualbefore the arithmetic encoding. Arithmetic encoder 7069 may generate andencode various kinds of head information. Arithmetic encoder 7069 mayarithmetically encode prediction mode information (PredMode) thatindicates the prediction mode used for the encoding by predictor 7063,and add the information to the bitstream.

ΔQP calculator 7070 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7069 and the predetermined desired code amount. The quantizationby quantizer 7065 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7069 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 71 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes arithmetic decoder 7071, LoDsetter 7072, searcher 7073, predictor 7074, inverse quantizer 7075,reconstructor 7076, and memory 7077.

Arithmetic decoder 7071 arithmetically decodes ZeroCnt and theprediction residual included in the bitstream. Arithmetic decoder 7071also decodes various kinds of header information. Arithmetic decoder7071 also arithmetically decodes prediction mode information (PredMode)from the bitstream, and outputs the obtained prediction mode informationto predictor 7074. Arithmetic decoder 7071 also decodes Delta_Layer,ADelta_QP, and NumPointADelta from the header of the bitstream.

LoD setter 7072 generates a LoD using decoded geometry information on athree-dimensional point. Searcher 7073 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints.

Predictor 7074 generates a predicted value of attribute information of acurrent three-dimensional point to be decoded. Inverse quantizer 7075inverse-quantizes the arithmetically decoded prediction residual.Specifically, inverse quantizer 7075 performs inverse quantization usinga quantization parameter based on the decoded Delta_Layer, ADelta_QP,and NumPointADelta.

Reconstructor 7076 generates a decoded value by summing the predictedvalue and the inverse-quantized prediction residual. Memory 7077 storesthe value (decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7077 is used for prediction ofa three-dimensional point yet to be decoded by predictor 7074.

In the following, an example in which RAHT layers are used instead ofthe LoD layers will be described. FIG. 72 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a finer unit when attribute information is encoded using RAHT. Forexample, when encoding attribute information using RAHT, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each RAHT layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on three-dimensional points by changing thequantization parameter for each three-dimensional point according to thedesired code amount and the actual code amount, for example. In thisway, the three-dimensional data encoding device can finally generate abitstream having a code amount close to the desired code amount. Thethree-dimensional data decoding device can properly decode the bitstreamby decoding QPbase, Delta_Layer, and ADelta included in the header togenerate the quantization parameters used by the three-dimensional dataencoding device.

For example, quantized value QP4 of NO-th attribute information iscalculated according to QP4=QP3+ADelta_QP[0]. Each ADelta_QP[i] may bethe difference value with respect to QPbase, likeQP4=QPbase+ADelta_QP[0].

FIG. 73 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 72 is used. The attribute information header shownin FIG. 73 is basically the same as the attribute information headershown in FIG. 63 but differs in that RAHT layers are used instead of LoDlayers.

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the layer to whichthree-dimensional point A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

FIG. 74 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 72 is used. Note that the attribute informationheader shown in FIG. 74 is basically the same as the attributeinformation header shown in FIG. 64 but differs in that RAHT layers areused instead of LoD layers.

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus 1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 75 is a flowchart of a three-dimensional data encoding process inthe case where RAHT is used. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7061). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7062). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7063). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 76 is a flowchart of the attribute information encoding process(S7063). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by Haar transformation(S7071).

The three-dimensional data encoding device then applies quantization tothe coding coefficient (S7072). The three-dimensional data encodingdevice then generates encoded attribute information (bitstream) byencoding the quantized coding coefficient (S7073).

The three-dimensional data encoding device then determines ΔQP (S7074).Note that the method of determining ΔQP is the same as step S7019 in thecase where LoD layers are used. Determined ΔQP is used for determining aquantization parameter used for quantization of a subsequent codingcoefficient.

The three-dimensional data encoding device applies inverse quantizationto the quantized coding coefficient (S7075). The three-dimensional dataencoding device then decodes the attribute information by applyinginverse Haar transformation to the inverse-quantized coding coefficient(S7076). For example, the decoded attribute information is referred toin the subsequent encoding.

FIG. 77 is a flowchart of a three-dimensional data decoding process inthe case where RAHT is used. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7065). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7066). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device decodes the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 78 is a flowchart of the attribute information decoding process(S7066). First, the three-dimensional data decoding device decodes thecoding coefficient from the bitstream (S7081). The three-dimensionaldata decoding device then decodes ΔQP from the bitstream (S7082).Specifically, the three-dimensional data decoding device decodesDelta_Layer, ADelta_QP, and NumPointADelta from the header of thebitstream.

The three-dimensional data decoding device then applies inversequantization to the coding coefficient (S7083). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7082 is used. The three-dimensional data decoding device thendecodes the attribute information by applying inverse Haartransformation to the inverse-quantized coding coefficient (S7084).

FIG. 79 is a block diagram of attribute information encoder 7023 in thecase where RAHT is used. Attribute information encoder 7023 includessorter 7081, Haar transformer 7082, quantizer 7083, inverse quantizer7084, inverse Haar transformer 7085, memory 7086, arithmetic encoder7087, and ΔQP calculator 7088.

Sorter 7081 generates a Morton code using geometry information on athree-dimensional point, and sorts a plurality of three-dimensionalpoints in the order of Morton codes. Haar transformer 7082 generates acoding coefficient by applying Haar transformation to attributeinformation. Quantizer 7083 quantizes the coding coefficient of theattribute information.

Inverse quantizer 7084 inverse-quantizes the quantized codingcoefficient. Inverse Haar transformer 7085 applies inverse Haartransformation to the coding coefficient. Memory 7086 stores values ofthe decoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7086 may be used forprediction or the like of a three-dimensional point yet to be encoded.

Arithmetic encoder 7087 calculates ZeroCnt from the quantized codingcoefficient, and arithmetically encodes ZeroCnt. Arithmetic encoder 7087also arithmetically encodes any quantized coding coefficient that is notzero. Arithmetic encoder 7087 may binarize the coding coefficient beforethe arithmetic encoding. Arithmetic encoder 7087 may generate and encodevarious kinds of head information.

ΔQP calculator 7088 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7087 and the predetermined desired code amount. The quantizationby quantizer 7083 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7087 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 80 is a block diagram of attribute information decoder 7043 in thecase where RAHT is used. Attribute information decoder 7043 includesarithmetic decoder 7091, inverse quantizer 7092, inverse Haartransformer 7093, and memory 7094.

Arithmetic decoder 7091 arithmetically decodes ZeroCnt and the codingcoefficient included in the bitstream. Arithmetic decoder 7091 maydecode various kinds of header information. Arithmetic decoder 7091 alsodecodes Delta_Layer, ADelta_QP, and NumPointADelta from the header ofthe bitstream.

Inverse quantizer 7092 inverse-quantizes the arithmetically decodedcoding coefficient. Specifically, inverse quantizer 7092 performs theinverse quantization using a quantization parameter based on the decodedDelta_Layer, ADelta_QP, and NumPointADelta.

Inverse Haar transformer 7093 applies inverse Haar transformation to theinverse-quantized coding coefficient. Memory 7094 stores values of thedecoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7094 may be used forprediction of a three-dimensional point yet to be decoded.

In the following, a variation of this embodiment will be described. Thethree-dimensional data encoding device may encode a quantizationparameter of attribute information on each three-dimensional point asnew attribute information.

In the following, an example of a process performed by thethree-dimensional data encoding device in this case will be described.The three-dimensional data encoding device encodes attribute informationA (such as color) by calculating a quantization parameter according tothe flow shown in FIG. 66. In this process, as a new attribute value ofeach three-dimensional point, the three-dimensional data encoding deviceencodes the quantization parameter used. In this case, thethree-dimensional data encoding device may perform the encoding bychanging the value of the quantization parameter for eachthree-dimensional point. For example, if the cumulative code amount isgreater than the value of the desired code amount×TH1, thethree-dimensional data encoding device can set the value of thequantization parameter to be greater, in order to reduce the actual codeamount. If the cumulative code amount is smaller than the value of thedesired code amount×TH3, the three-dimensional data encoding device canset the value of the quantization parameter to be smaller, in order toincrease the actual code amount.

After the encoding of attribute information A, the three-dimensionaldata encoding device encodes the quantization parameter assigned to eachthree-dimensional point as new attribute information A′. In thisprocess, the three-dimensional data encoding device may apply losslessencoding to prevent losing of the amount of information on thequantization parameters. The three-dimensional data encoding device mayadd, to the header or the like, information that indicates that theencoded attribute information is a quantization parameter. In this way,the three-dimensional data decoding device can properly decode thequantization parameter used by the three-dimensional data encodingdevice.

When performing predictive encoding of attribute information using Nthree-dimensional points in the periphery of a current three-dimensionalpoint, the three-dimensional data encoding device may encode aquantization parameter on the supposition that N=1. In this way, thecalculation amount can be reduced.

Next, an example of a process performed by the three-dimensional datadecoding device will be described. First, the three-dimensional datadecoding device decodes attribute information A′ among the attributeinformation in the bitstream to obtain a quantization parameter used forthe decoding of attribute information A. The three-dimensional datadecoding device then decodes attribute information A using the decodedquantization parameter.

Note that the three-dimensional data encoding device may encode, as newattribute information A′, ΔQP, which is the amount of change of thequantization parameter between three-dimensional points, instead of thequantization parameter described above. When ΔQP assumes a positive ornegative value, the three-dimensional data encoding device may transformsigned ΔQP into a positive value before encoding ΔQP as described below.When signed ΔQP (deltaQP_s) is smaller than 0, unsigned ΔQP (deltaQP_u)is set to be −1−(2×deltaQP_s). When signed ΔQP (deltaQP_s) is equal toor greater than 0, unsigned ΔQP (deltaQP_u) is set to be 2×deltaQP_s.

The three-dimensional data encoding device may encode, as attributeinformation, a quantization parameter used for encoding of eachattribute information. For example, the three-dimensional data encodingdevice may encode a quantization parameter of attribute information A oncolor as attribute information A′, and encode a quantization parameterof attribute information B on reflectance as attribute information B′.In this way, the quantization parameter can be changed for eachattribute information. For example, if the quantization parameter ofattribute information having higher priority is set to be smaller, andthe quantization parameter of attribute information having lowerpriority is set to be greater, the total code amount can be reducedwhile preserving the attribute information having higher priority.

When quantizing and encoding a prediction residual for attributeinformation on a three-dimensional point, if delta_Layer_present_flag,additional_delta_QP_present_flag and the like indicate that Delta_Layerand ADelta_QP are set in the header, the three-dimensional data encodingdevice need not use the value of a quantization weight (QW) thatindicates the importance of a three-dimensional point. For example, whenQW is used, the quantization parameter is set to be smaller when QW isgreater (the importance is higher). In this way, it can be chosenwhether to perform the quantization based on the importance determinedby an internal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user.

The three-dimensional data encoding device may add, to the header, aflag that indicates whether to use the value of the quantization weight(QW) or not. In this way, it can be chosen whether to perform thequantization by combining the values of Delta_Layer and ADelta_QP and QWor not, the two manners can be selectively used depending on the purposeof the user.

When quantizing and encoding a transformation coefficient for attributeinformation on a three-dimensional point using RAHT or the like, ifdelta_Layer_present_flag, additional_delta_QP_present_flag and the likeindicate that Delta_Layer and ADelta_QP are set in the header, thethree-dimensional data encoding device need not use the value of thequantization weight (QW). In this way, it can be chosen whether toperform the quantization based on the importance determined by aninternal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user. Furthermore, the three-dimensionaldata encoding device may add, to the header, a flag that indicateswhether to use the value of quantization weight (QW) or not. In thisway, it can be chosen whether to perform the quantization by combiningthe values of Delta_Layer and ADelta_QP and QW or not, the two mannerscan be selectively used depending on the purpose of the user.

FIG. 81 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in this case. Theattribute information header shown in FIG. 81 differs from the attributeinformation header shown in FIG. 64 in that the attribute informationheader further includes default_delta_Layer_present_flag,default_delta_Layer_index, default_additional_delta_QP_present_flag, anddefault_additional_delta_QP_index.

default_delta_Layer_present_flag is a flag that indicates whether to usean initially set value of Delta_Layer defined by a standard or the likeor not. For example, a value of 1 indicates that initially setDelta_Layer is to be used. A value of 0 indicates that initially setDelta_Layer is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting Delta_Layer to be 0, for example.

default_delta_Layer_index is information that allows identification ofDelta_Layer to be used among one or more initially set values ofDelta_Layer defined by a standard or the like. For example,default_delta_Layer_index is defined as described below.

When default_delta_Layer_index=0, Delta_Layer for all layers is set tobe 1. That is, the value of the quantization parameter increases by 1every time a layer is incremented. When default_delta_Layer_index=1,Delta_Layer for all layers is set to be 2. That is, the value of thequantization parameter increases by 2 every time a layer is incremented.

If an initially set Delta_Layer is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of Delta_Layer to the header, so that the code amount of theheader can be reduced.

default_additional_delta_QP_present_flag is a flag that indicateswhether to use an initially set value of ADelta_QP defined by a standardor the like or not. For example, a value of 1 indicates that initiallyset ADelta_QP is to be used. A value of 0 indicates that initially setADelta_QP is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting ADelta_QP to be 0, for example.

default_additional_delta_QP_index is information that allowsidentification of ADelta_QP to be used among one or more values ofinitially set ADelta_QP defined by a standard or the like. For example,default_additional_delta_QP_index is defined as described below.

When default_additional_delta_QP_index=0, ADelta_QP is set to be 1 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 1 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

When default_additional_delta_QP_index=1, ADelta_QP is set to be 2 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 2 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

If an initially set ADelta_QP is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of ADelta_QP to the header, so that the code amount of the headercan be reduced.

Embodiment 9

FIG. 82 is a diagram for illustrating a schematic configuration of anattribute information encoder according to this embodiment. FIG. 83 is adiagram for illustrating a schematic configuration of an attributeinformation decoder according to this embodiment.

To improve the coding efficiency, attribute information in the pointcloud compression (PCC) is transformed in various manners (such aslifting, RAHT, or other transformation processes). The transformationprocess has a strong “energy compression” property. As a result of thetransformation process, important signal information is included in alow frequency component. A high frequency component is quantized inorder to reduce the number of bits that occur.

In this embodiment, in order to further improve the coding efficiency,that is, in order to minimize the high frequency components that are tobe transformed by maximizing the relationship between the positions ofthree-dimensional points, the three-dimensional data encoding devicemodifies the order of the three-dimensional points in input point clouddata (referred to also simply as a point cloud, hereinafter) or, morespecifically, the order of the transformation process performed on thethree-dimensional points by using geometry information.

First, point cloud re-ordering unit 7401 of the three-dimensional dataencoding device performs a process (re-ordering process (re-ordering))of modifying the order of the three-dimensional points in input pointcloud data, in which a plurality of three-dimensional points arearranged in a predetermined order. For example, pieces of data thatindicate the three-dimensional points in a point cloud input to thethree-dimensional data encoding device are arranged in a predeterminedorder. Point cloud re-ordering unit 7401 re-orders the pieces of dataindicating the three-dimensional points in the input point cloud in apredetermined manner.

Transformer 7402 of the three-dimensional data encoding device thenperforms a transformation process (transform) on the point cloudre-ordered by the re-ordering process.

Quantizer 7403 of the three-dimensional data encoding device thenperforms a quantization process.

Entropy encoder 7404 of the three-dimensional data encoding device thenperforms an entropy encoding process. For example, entropy encoder 7404transmits a bitstream (encoded bitstream) including encoded point clouddata to the three-dimensional data decoding device.

Entropy decoder 7411 of the three-dimensional data decoding deviceperforms an entropy decoding process on the encoded point cloud dataincluded in the bitstream received from the three-dimensional dataencoding device, for example.

Inverse quantizer 7412 of the three-dimensional data decoding devicethen performs an inverse quantization process.

Inverse transformer 7414 of the three-dimensional data decoding devicethen performs an inverse transformation process (inverse transform).

Point cloud re-ordering unit 7413 of the three-dimensional data decodingdevice then performs a re-ordering process (ordering process) on thepoint cloud data having been subjected to the inverse transformationprocess. The re-ordering process here is a reverse process to there-ordering process in the three-dimensional data encoding device. Byre-ordering the decoded point cloud data in this way, thethree-dimensional data decoding device can generate point cloud data inwhich the three-dimensional points are arranged in the same order asthose in the point cloud data input to the three-dimensional dataencoding device. The three-dimensional data decoding device transmits(outputs) the point cloud data subjected to the re-ordering process toanother device, for example. In this way, the other device can obtainpoint cloud data in which the three-dimensional points are arranged inthe same order as those in the point cloud data input to thethree-dimensional data encoding device.

Note that the three-dimensional data decoding device may perform there-ordering process for the point cloud in the same manner as in thethree-dimensional data encoding device after performing the inversetransformation process.

For example, the three-dimensional data encoding device performs there-ordering process by generating re-ordering information that indicatesthe order of points in a point cloud re-ordered using geometryinformation obtained by encoding and decoding of the point cloud. Forexample, the three-dimensional data decoding device performs there-ordering process by generating re-ordering information in the samemanner as in the three-dimensional data encoding device using thedecoded geometry information.

In this way, the three-dimensional data decoding device can generate andoutput a point cloud (point cloud data) in which pieces of data arearranged in the same order as the pieces of data in the point cloudinput to the three-dimensional data encoding device.

Note that when the three-dimensional data decoding device does not needto generate a point cloud arranged in the same order as the point cloudinput to the three-dimensional data encoding device, thethree-dimensional data decoding device can omit the re-ordering process.

In this way, the three-dimensional data decoding device can reduce theprocessing amount.

The three-dimensional data encoding device may add the re-orderinginformation to the bitstream. The re-ordering information is informationthat indicates the data order of pieces of attribute information on aplurality of three-dimensional points in the point cloud data input tothe three-dimensional data encoding device (that is, the point clouddata yet to be re-ordered). The three-dimensional data decoding devicemay perform the re-ordering process based on the re-ordering informationdecoded from the bitstream. In this way, the three-dimensional datadecoding device can reduce the amount of processing for generating there-ordering information.

The method of modifying the ordering of the point cloud is not limitedto the method described above.

FIG. 84 is a diagram for illustrating a schematic configuration of anattribute information encoder according to a variation of thisembodiment. FIG. 85 is a diagram for illustrating a schematicconfiguration of an attribute information decoder according to avariation this embodiment.

To improve the coding efficiency, attribute information in the PCC istransformed in various manners (such as lifting, RAHT, or othertransformation processes). The transformation process has a strong“energy compression” property. As a result of the transformationprocess, important signal information is included in a low frequencycomponent. A high frequency component is quantized in order to reducethe number of bits that occur.

In order to further improve the coding efficiency, the three-dimensionaldata encoding device modifies the order of points in an input pointcloud (point cloud data) using geometry information to maximize therelationship between the positions of three-dimensional points tominimize the high frequency components that are to be transformed.

First, point cloud attribute swapper 7421 of the three-dimensional dataencoding device performs a process (swapping process) of modifying theorder of the pieces of attribute information on the three-dimensionalpoints in input point cloud data (referred to also simply as a pointcloud, hereinafter), in which a plurality of three-dimensional pointsare arranged in a predetermined order. The swapping process (swapping)is an example of the re-ordering process. For example, pieces of datathat indicate the three-dimensional points in a point cloud input to thethree-dimensional data encoding device are arranged in a predeterminedorder (such as an order of Morton codes). Point cloud attribute swapper7421 re-orders only the pieces of attribute information in apredetermined manner, without modifying the order of the Morton codes,the pieces of geometry information or the like in the input point clouddata indicating the three-dimensional points in the point cloud, forexample.

For example, point cloud attribute swapper 7421 performs the swappingprocess of swapping pieces of attribute information without changing theMorton codes assigned to the three-dimensional points, before performingthe transformation process for the attribute information of the pointcloud.

For example, when Morton codes 0, 1, and 2 are assigned tothree-dimensional points A, B, and C, respectively, if thethree-dimensional data encoding device determines that point A and pointC are close to each other based on the geometry information or the like,the three-dimensional data encoding device performs a swapping processof swapping pieces of attribute information on point B and point Cbefore performing a transformation process, such as RAHT, using theattribute information on point A and point B.

In this way, the three-dimensional data encoding device can reduce thecoefficient of a high frequency component subjected to thetransformation process, and therefore can improve the coding efficiency.

Note that the three-dimensional data encoding device may add, to theheader or the like of the bitstream, swapping information that indicatesthe way in which the three-dimensional points have been swapped.

In this way, the three-dimensional data decoding device canappropriately reassign the pieces of attribute information to thethree-dimensional points (that is, perform the re-ordering process)using the swapping information decoded from the header of the bitstreamafter the inverse transformation process.

Note that the three-dimensional data encoding device may add theswapping information to the header using variable length encoding or thelike.

In this way, the three-dimensional data encoding device can reduce theheader amount.

Transformer 7422 of the three-dimensional data encoding device thenperforms a transformation process on the point cloud re-ordered by theswapping process.

Quantizer 7423 of the three-dimensional data encoding device thenperforms a quantization process.

Entropy encoder 7424 of the three-dimensional data encoding device thenperforms an entropy encoding process. For example, entropy encoder 7424transmits a bitstream including encoded point cloud data to thethree-dimensional data decoding device.

Entropy decoder 7431 of the three-dimensional data decoding deviceperforms an entropy decoding process on the encoded point cloud dataincluded in the bitstream received from the three-dimensional dataencoding device, for example.

Inverse quantizer 7432 of the three-dimensional data decoding devicethen performs an inverse quantization process.

Inverse transformer 7433 of the three-dimensional data decoding devicethen performs an inverse transformation process.

After the inverse transformation process is performed, point cloudattribute swapper 7434 of the three-dimensional data decoding devicethen performs a swapping process of swapping pieces of attributeinformation in the same manner as in the three-dimensional data encodingdevice, for example.

For example, when Morton codes 0, 1, and 2 are assigned to decodedthree-dimensional points A, B, and C, respectively, if thethree-dimensional data decoding device determines that point A and pointC are close to each other based on the decoded geometry information orthe like, the three-dimensional data decoding device swaps the pieces ofdecoded attribute information on point B and point C with each other.

In this way, the three-dimensional data decoding device can decodethree-dimensional points with appropriate attribute information assignedthereto. The three-dimensional data decoding device can also generateand output a point cloud (point cloud data) in which pieces of data arearranged in the same order as the pieces of data in the point cloudinput to the three-dimensional data encoding device.

Note that when the three-dimensional data decoding device does not needto generate a point cloud arranged in the same order as the point cloudinput to the three-dimensional data encoding device, thethree-dimensional data decoding device can omit the swapping process.

In this way, the three-dimensional data decoding device can reduce theprocessing amount.

The three-dimensional data encoding device may add the swappinginformation to the bitstream. The three-dimensional data decoding devicemay perform the swapping process based on the swapping informationdecoded from the bitstream.

In this way, the three-dimensional data decoding device can reduce theamount of processing for generating the swapping information.

FIG. 86 is a diagram for illustrating the re-ordering process accordingto this embodiment. Specifically, part (a) of FIG. 86 is a diagramshowing an example of a plurality of voxels (specifically, eight voxels)and Morton codes assigned to the plurality of voxels. Part (b) of FIG.86 is a diagram showing another example of a plurality of voxels(specifically, eight voxels) and Morton codes assigned to the pluralityof voxels. Part (c) of FIG. 86 is a diagram showing an example of anordering of a point cloud obtained by modifying the ordering of thepoint cloud shown in part (b) of FIG. 86.

In each of parts (a) and (b) of FIG. 86, the left part shows theplurality of voxels, and the right part shows the ordering of the pointcloud. Note that the ordering of the point cloud is indicated by Mortoncodes.

As shown in parts (a) and (b) of FIG. 86, for example, the point cloudis arranged in a Z order or Morton order before the transformationprocess is performed. Here, in part (b) of FIG. 86, for example, inorder to effectively perform the transformation process, thethree-dimensional data encoding device performs the re-ordering processto further re-order the point cloud arranged in a Morton order based onthe geometry information on the three-dimensional points as shown inpart (c) of FIG. 86, for example.

Part (a) of FIG. 86 shows an example of a point cloud arranged in aMorton order. Specifically, the example shown in part (a) of FIG. 86 isan example in which each voxel includes one three-dimensional point. Inthis case, for example, the Morton order of the point cloud is 0, 1, 2,3, 4, 5, 6, 7, and the point cloud is arranged in this order.

On the other hand, part (b) of FIG. 86 shows a Morton order in the casewhere all the voxels are not occupied by a three-dimensional point.Specifically, voxel 1 and voxel 2 are not occupied by anythree-dimensional point. In this case, for example, the Morton order ofthe point cloud is 0, 3, 4, 5, 6, 7, and the point cloud is arranged inthis order.

The attribute information concerning the position of a three-dimensionalpoint is obtained from the surface of a three-dimensional object, forexample. Therefore, attribute information on three-dimensional points(closest three-dimensional points) whose surfaces are the closest toeach other are highly correlated to each other. That is, attributeinformation on three-dimensional points that are close to each other arelikely to have values close to each other. Therefore, thethree-dimensional data encoding device performs the re-ordering processof re-ordering the point cloud so that such three-dimensional points atclose positions are brought closer to each other, before performing thetransformation process. For example, the three-dimensional data encodingdevice re-orders the point cloud from the order of 0, 3, 4, 5, 6, 7shown in part (b) of FIG. 86 to the order of 0, 4, 3, 5, 6, 7 shown inpart (c) of FIG. 86. That is, the three-dimensional data encoding deviceswaps the positions of the pieces of data (geometry information,attribute information or the like) on the three-dimensional pointassigned with a Morton code of 3 and the three-dimensional pointassigned with a Morton code of 4 with each other.

Note that the “re-ordering process” in this example may refer to aprocess of generating re-ordering information that indicates the orderof a new point cloud generated by performing the re-ordering process ona point cloud arranged in a Morton order using distance information,geometry information or the like on three-dimensional points, forexample.

FIG. 87 is a diagram for illustrating a first example of thetransformation process for attribute information according to thisembodiment. FIG. 87 shows a hierarchical structure with which thethree-dimensional data encoding device performs the transformationprocess, for example.

The three-dimensional data encoding device performs the transformationprocess on the point cloud subjected to the re-ordering process. Thetransformation process is RAHT (region adaptive Haar transformation),for example.

The transformation process is expressed by the following formula(Equation M1), for example.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack & \; \\{\begin{bmatrix}L_{l,m} \\H_{l,m}\end{bmatrix} = {\begin{bmatrix}\alpha & \beta \\{- \beta} & \alpha\end{bmatrix}\begin{bmatrix}C_{{l + 1},{2m}} \\C_{{l + 1},{{2m} + 1}}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{M1}} \right)\end{matrix}$

α and β each represent an arbitrary number, and coefficients(transformation coefficients) represented by α and β can be updated. 1represents a value that indicates a level of a layer. m represents avalue that indicates the order of three-dimensional points in eachlayer. Cl, m represents a value that indicates attribute information onthe m-th three-dimensional point at level 1.

A low pass sub-band (low frequency component) at level 1 is expressed bythe following formula (Equation M2).

[Math. 3]

L _(l,m) =αC _(l+1,2m) +βC _(l+1,2m+1)  (Equation M2)

A high pass sub-band (high frequency component) at level 1 is expressedby the following formula (Equation M3).

[Math. 4]

H _(l,m) =αC _(l+1,2m+1) −βC _(l−1,2m)  (Equation M3)

The high pass sub-band is quantized and entropy-encoded. On the otherhand, the low pass sub-band is moved to the next level as shown by thefollowing formula (Equation M4).

[Math. 5]

C _(l,m) =L _(l,m).  (Equation M4)

For example, the low pass sub-band and the high pass sub-band for0-th(m=0) at level 1=2 is expressed by the following formulas (EquationM5) and (Equation M6), respectively.

[Math. 6]

L _(2,0) =αC _(3,0) +βC _(3,1)  (Equation M5)

H _(2,0) =αC _(3,1) −βC _(3,0)  (Equation M6)

For example, when the positions of three-dimensional points are close toeach other, such as those having attribute information C3,0 andattribute information C3,1, those pieces of attribute information arelikely to be similar (or likely to have similar attribute values).Therefore, H, which is the value of the high pass sub-band calculatedfrom the difference between the attribute information C3, 0 and theattribute information C3, 1, is likely to be small.

FIG. 88 is a diagram for illustrating a second example of thetransformation process for attribute information according to thisembodiment. Specifically, part (a) of FIG. 88 is a diagram showing anordering of point cloud data yet to be subjected to the re-orderingprocess, and part (b) of FIG. 88 is a diagram showing an ordering ofpoint cloud data obtained by performing the re-ordering process on thepoint cloud data shown in part (a) of FIG. 88.

In order to further improve the coding efficiency of attributeinformation, for example, the three-dimensional data encoding deviceperforms the re-ordering process on attribute information based ondistance or geometry information on three-dimensional points.

For example, as shown in part (a) of FIG. 88, it is assumed that (n+1)pieces of point data are arranged in ascending order of Morton codes of0 to n.

For example, the three-dimensional data encoding device performs there-ordering process of re-ordering the order of the Morton codes (thatis, the pieces of data on three-dimensional points) based on distance orgeometry information on three-dimensional points.

In this way, for example, the three-dimensional data encoding devicegenerates new point cloud data in which the position of the data on thethree-dimensional point assigned with a Morton code of 1 and theposition of the data on the three-dimensional point assigned with aMorton code of 2 are swapped with each other, and the position of thedata on the three-dimensional point assigned with a Morton code of 7 andthe position of the data on the three-dimensional point assigned with aMorton code of 9 are swapped with each other.

Alternatively, the three-dimensional data encoding device may performthe re-ordering process of modifying the order of the pieces ofattribute information, and retain re-ordering information that allowsencoding and decoding as additional information (Meta data).

FIG. 89 is a diagram for illustrating a third example of thetransformation process for attribute information according to thisembodiment. Part (a) of FIG. 89 is a diagram showing an ordering ofpoint cloud data yet to be subjected to the re-ordering process, andpart (b) of FIG. 89 is a diagram showing an ordering of point cloud dataobtained by performing the re-ordering process on the point cloud datashown in part (a) of FIG. 89. Note that FIG. 89 is a diagram showing anexample in which a point cloud is re-ordered based on the distances(distance information) between three-dimensional points. For example, asshown in part (a) of FIG. 89, it is assumed that (n+1) pieces of pointdata are arranged in ascending order of Morton codes of 0 to n.

First, the three-dimensional data encoding device designates thethree-dimensional point located at position 0 (that is, thethree-dimensional point assigned with a Morton code of 0) as a referencepoint, and searches (by calculation) k three-dimensional pointsneighboring to the reference point (k=5 in this example) for thethree-dimensional point closest to the reference point (closestthree-dimensional point). In this example, it is assumed that thethree-dimensional point located at position 2 is the three-dimensionalpoint closest to position 0. In this case, for example, thethree-dimensional data encoding device moves the data on thethree-dimensional point located at position 2 to next to position 0,which is the position of the reference point.

Note that the three-dimensional data encoding device may add k, whichindicates the number of the three-dimensional points in the searchrange, to the header of the bitstream.

In this way, the three-dimensional data decoding device can perform there-ordering process for the three-dimensional points using the samesearch range as the three-dimensional data encoding device by decodingsearch range k included in the header of the bitstream.

The three-dimensional data encoding device then designates thethree-dimensional point located at position 1 as a reference point, andsearches k three-dimensional points neighboring to the reference pointfor the closest three-dimensional point. In this example, it is assumedthat the three-dimensional point located at position 3 is thethree-dimensional point closest to position 1. In this case, forexample, the data on the three-dimensional point located at position 3is located next to the data on the three-dimensional point located atposition 1, which is the reference point, so that the three-dimensionaldata encoding device then designates the three-dimensional point locatedat position 3 as a new reference point, and searches for thethree-dimensional point closest to the new reference point.

As described above, the three-dimensional data encoding device performsthe setting of a reference point and the searching for thethree-dimensional point closest to the set reference point until thethree-dimensional point located at position n is reached, for example.

FIG. 90 is a diagram for illustrating a fourth example of thetransformation process for attribute information according to thisembodiment. Part (a) of FIG. 90 is a diagram showing an ordering ofpoint cloud data yet to be subjected to the re-ordering process, andpart (b) of FIG. 90 is a diagram showing an ordering of point cloud dataobtained by performing the re-ordering process on the point cloud datashown in part (a) of FIG. 90.

Note that FIG. 90 is a diagram showing an example in which a point cloudis re-ordered based on the distances (distance information) betweenthree-dimensional points. For example, as shown in part (a) of FIG. 90,it is assumed that (n+1) pieces of point data are arranged in ascendingorder of Morton codes of 0 to n.

First, the three-dimensional data encoding device designates thethree-dimensional point located at position 0 as a reference point, andsearches k three-dimensional points close to the reference point for theclosest three-dimensional point. In this example, it is assumed that thethree-dimensional point located at position 24 is the three-dimensionalpoint closest to position 0.

For example, the three-dimensional data encoding device then designatesthe three-dimensional point located at position 24 as a reference point,and searches k three-dimensional points close to the reference point forthe closest three-dimensional point. The three-dimensional data encodingdevice performs the re-ordering process described above for all thethree-dimensional points one by one.

Note that the three-dimensional data encoding device may perform there-ordering process for the point cloud based on Morton codes.Alternatively, the three-dimensional data encoding device may performthe re-ordering process only on the attribute information on thethree-dimensional points, and maintain the positions of the Mortoncodes. Alternatively, the three-dimensional data encoding device maygenerate swapping table information (referred to also as a re-orderingtable), which is swapping information on a point cloud that indicatesthe positions of the three-dimensional points in the point cloud yet tobe subjected to the re-ordering process.

As described above, for example, the three-dimensional data encodingdevice performs the re-ordering process on a point cloud based onthree-dimensional distances. For example, as shown in part (a) of FIG.90, it is assumed that (n+1) pieces of point data are arranged inascending order of Morton codes of 0 to n. For example, thethree-dimensional data encoding device designates the three-dimensionalpoint located at position 0 as a reference point, and searches kthree-dimensional points close to the reference point for the closestthree-dimensional point.

The three-dimensional data encoding device then expresses X0=(x0, y0,z0) for the three-dimensional point at position 0 as Xi=(xi, yi, zi) forthe three-dimensional point at position i.

Among the k three-dimensional points close to the three-dimensionalpoint located at position 0, the three-dimensional point closest to thethree-dimensional point located at position 0 can be determined bysearching for the minimum value of the Euclidean distances between Ithree-dimensional points as shown by the following formulas (EquationM7) and (Equation M8).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 7} \right\rbrack & \; \\{{\min\limits_{i}{{X_{0} - X_{i}}}_{2}},{{{where}\mspace{14mu}{\mathfrak{i}}} \in \left\{ {1,\ldots\mspace{14mu},\ k} \right\}}} & \left( {{Equation}\mspace{14mu}{M7}} \right) \\{{{X_{0} - X_{i}}}_{2} = \sqrt{\left( {x_{0} - x_{i}} \right)^{2} + \left( {y_{0^{-}}y_{i}} \right)^{2} + \left( {z_{0} - z_{i}} \right)^{2}}} & \left( {{Equation}\mspace{14mu}{M8}} \right)\end{matrix}$

Once the closest three-dimensional point is determined, the closestthree-dimensional point is moved from position i to position 1. Notethat another method may be used for determining the three-dimensionaldistances.

FIG. 91 is a diagram for illustrating a fifth example of thetransformation process for attribute information according to thisembodiment. FIG. 92 is a diagram for illustrating examples of aconnection between voxels and normal vectors in the examples accordingto this embodiment.

Note that FIGS. 91 and 92 are diagrams showing an example in which apoint cloud is re-ordered using geometry information onthree-dimensional points.

Neighboring three-dimensional points on the surface of the same objectare likely to have similar attribute information, and therefore, theordering of the point cloud data is modified so that the pieces of dataon the neighboring three-dimensional points are close to each otherbefore the transformation process in order to improve the codingefficiency. For example, a reference point (voxel) Cr that hasneighboring three-dimensional points Ca, Cb, Cc, Cd, Ce, Cf, and Cg willbe considered. Geometry information, such as a normal vector

[Math. 8]

{right arrow over (N _(r))}  (Equation M9),

is used to determine a surface of a three-dimensional point.

For example, in the re-ordering process, points Ca, Cb, and Cd have anormal vector oriented in the same direction as the normal vector ofpoint Cr and is of a connection type in which the objects of the pointsare connected to the object of point Cr, so that the point cloud data isre-ordered so that the pieces of data on these points are close to pointCr.

The normal vector (Equation M9) of point Cr is calculated according tothe following formula (Equation M10).

[Math. 9]

{right arrow over (N _(r))}=(C _(b) −C _(r))×(C _(a) −C _(r))  (EquationM10)

Note that “×” in the formula (Equation M10) represents vector product.

Voxels may be grouped based on the normal vector and the objectconnection type. In that case, for example, the three-dimensional pointsin the point cloud data are re-ordered based on the groups resultingfrom the grouping.

As shown in FIG. 92, connection types include a stair-like type(Stair-like), a convex type (Convex), and a concave type (Concave), forexample.

For example, the connection types described above are used for groupingfor the re-ordering process. For example, three-dimensional pointsrelated in the convex connection type are grouped into the same group.On the other hand, for example, three-dimensional points related in theconcave connection type or stair-like connection type are not groupedinto the same group. Three-dimensional points related in the concaveconnection type or stair-like connection type are likely to belong todifferent objects or have different attribute information. For example,the concave connection type is likely to be more strongly affected by ashadow than the convex connection type. Therefore, in the grouping of apoint cloud, three-dimensional points related in the concave connectiontype are not grouped into the same group but separated from each other.

Note that although an example has been described in which thethree-dimensional data encoding device calculates perpendicular vectors(normal vectors) and uses the calculated normal vectors for there-ordering process, the present disclosure is not necessarily limitedthereto. For example, when encoding and decoding normal vectors asattribute information, the three-dimensional data encoding device mayperform the re-ordering process for a point cloud by performing thegrouping described above using the values of the attribute information.

Note that the three-dimensional data encoding device may generate there-ordering information on the three-dimensional points yet to besubjected to the transformation process or the swapping information onthe attribute information on the three-dimensional points usinginformation used for encoding of the geometry information. For example,as described above, the three-dimensional data encoding device generatesthe re-ordering information or swapping information using neighboringnode information calculated when encoding the occupancy code for eachnode for the geometry information.

In this example, there is a possibility that the three-dimensional dataencoding device generates information that indicates that a nodeassigned with a Morton code of 1 is not occupied and a node assignedwith a Morton code of 4 is occupied when encoding an occupancy code fora node assigned with a Morton code of 0, for example. Therefore, basedon the information, the three-dimensional data encoding device maychoose the node assigned with the Morton code of 4 as a pair to the nodeassigned with the Morton code of 0 and apply the transformation to thenode. Alternatively, the attribute information on the node assigned withthe Morton code of 1 and the attribute information on the node assignedwith the Morton code of 4 may be swapped with each other, and thetransformation process may be performed on the node assigned with theMorton code of 0 and the node assigned with the Morton code of 1.

In this way, the three-dimensional data encoding device may perform thetransformation process by generating re-ordering information forthree-dimensional points or swapping information for attributeinformation using information generated when encoding geometryinformation.

With such a configuration, the three-dimensional data encoding devicecan improve the coding efficiency while reducing the amount ofprocessing for generation of the re-ordering information or swappinginformation.

The three-dimensional data decoding device may generate re-orderinginformation for three-dimensional points subjected to the inversetransformation process or swapping information for attribute informationon three-dimensional points subjected to the inverse transformationprocess using information used when decoding geometry information.

For example, as described above, the three-dimensional data decodingdevice generates re-ordering information or swapping information usingneighboring node information calculated when decoding an occupancy codefor each node for geometry information.

In this example, there is a possibility that the three-dimensional datadecoding device generates information that indicates that a nodeassigned with a Morton code of 1 is not occupied and a node assignedwith a Morton code of 4 is occupied when decoding an occupancy code fora node assigned with a Morton code of 0. Therefore, based on theinformation, the three-dimensional data decoding device may swap thethree-dimensional point assigned with the Morton code of 1 subjected tothe inverse transformation process and the three-dimensional pointassigned with the Morton code of 4 subjected to the inversetransformation process with each other. Alternatively, thethree-dimensional data decoding device may swap the attributeinformation on the node assigned with the Morton code of 1 and theattribute information on the node assigned with the Morton code of 4after the inverse transformation.

In this way, the three-dimensional data decoding device can properlydecode the bitstream encoded with improved coding efficiency, whilereducing the processing amount for the generation of re-orderinginformation or swapping information by swapping three-dimensional pointsor swapping pieces of attribute information after generating andinverse-transforming re-ordering information for the three-dimensionalpoints or swapping information for the attribute information based oninformation generated in the decoding of geometry information.

The following describes an adaptive entropy encoding process usinggeometry information of a three-dimensional point.

When local geometries of two nodes in a tree structure are similar toeach other, there is a chance that occupancy states (i.e., states eachindicating whether a three-dimensional point is included) of child nodesare similar to each other. As a result, the three-dimensional dataencoding device performs grouping using a local geometry of a parentnode. This enables the three-dimensional data encoding device to grouptogether the occupancy states of the child nodes, and use a differentcoding table for each group. Accordingly, it is possible to improve theentropy encoding efficiency.

FIG. 93 is a diagram illustrating an example of geometry information.Geometry information includes information indicating whether each ofneighboring nodes of a current node is occupied (i.e., includes athree-dimensional point). For example, the three-dimensional dataencoding device calculates a local geometry of the current node usinginformation indicating whether a neighboring node includes athree-dimensional point (is occupied or non-occupied). A neighboringnode is, for example, a node spatially located around a current node, ora node located in the same position in a different time as the currentnode or spatially located around the position.

In FIG. 93, a hatched cube indicates a current node. A white cube is aneighboring node, and indicates a node including a three-dimensionalpoint. In FIG. 93, the geometry pattern indicated in (2) is obtained byrotating the geometry pattern indicated in (1). Accordingly, thethree-dimensional data encoding device determines that these geometrypatterns have a high geometry similarity, and entropy encodes thegeometry patterns using the same coding table. In addition, thethree-dimensional data encoding device determines that the geometrypatterns indicated in (3) and (4) have a low geometry similarity, andentropy encodes the geometry patterns using other coding tables.

FIG. 94 is a diagram illustrating an example of occupancy codes ofcurrent nodes in the geometry patterns of (1) to (4) illustrated in FIG.93, and coding tables used for entropy encoding. As illustrated above,the three-dimensional data encoding device determines that the geometrypatterns of (1) and (2) are included in the same geometry group, anduses same coding table A for the geometry patterns of (1) and (2). Thethree-dimensional data encoding device uses coding table B and codingtable C for the geometry patterns of (3) and (4), respectively.

As illustrated in FIG. 94, there is a case in which the occupancy codesof the current nodes in the geometry patterns of (1) and (2) included inthe same geometry group are identical to each other.

FIG. 95 is a diagram for illustrating a sixth example of thetransformation process for attribute information according to thisembodiment.

The three-dimensional data encoding device may perform the re-orderingprocess for each layer. Alternatively, the three-dimensional dataencoding device may calculate a three-dimensional position used for there-ordering process for a layer based on a three-dimensional position ina layer lower than that layer.

For example, the three-dimensional position of each three-dimensionalpoint in layer 2 (level 1=2) is calculated according to the followingformulas (Equation M11), (Equation M12), and (Equation M13).

[Math. 10]

C _(2,0)=(C _(3,0) +C _(3,1))/2  (Equation M11)

C _(2,1)=(C _(3,2) +C _(3,3))/2  (Equation M12)

C _(2,2)=(C _(3,4) ±C _(3,5))/2  (Equation M13)

For example, the three-dimensional position of each three-dimensionalpoint in layer 1 (level 1=1) is calculated according to the followingformulas (Equation M14) and (Equation M15).

[Math. 11]

C _(1,0)=(C _(2,0) +C _(2,1))/2  (Equation M14)

C _(1,1) =C _(2,2)  (Equation M15)

FIG. 96 is a diagram for illustrating a seventh example of thetransformation process for attribute information according to thisembodiment. Note that in the point cloud data shown in FIG. 96, there isno three-dimensional point at a position assigned with a Morton code of5.

Each three-dimensional point that is not paired is not merged with anyother three-dimensional point during the transformation process. There-ordering process does not need to be performed for such athree-dimensional point. For example, the three-dimensional pointlocated at position 0 has a valid pair to the three-dimensional point,and the re-ordering process is performed on the three-dimensional pointlocated at position 4, which is the closest three-dimensional point. Onthe other hand, the three-dimensional point located at position 3 has novalid pair to the three-dimensional point, since there is nothree-dimensional point at position 5. In order to prevent thethree-dimensional point located at such position 3 from being used forthe transformation process, no closest three-dimensional point need tobe searched for.

To calculate which three-dimensional point has a valid pair for thetransformation process, for example, the following method is used.

Provided that the i-th Morton code at level 1 is denoted as M1, i,

If(Ml, i>>1)==(Ml, i+1>>1){Find the nearest point to pair with the point at Ml, i.Skip 1 position and move to next point.} else

For example, if the conditional expression holds for a three-dimensionalpoint, the three-dimensional point has a valid pair for thetransformation process, the three-dimensional data encoding devicesearches for the closest three-dimensional point. On the other hand, ifthe conditional expression does not hold for a three-dimensional point,for example, the three-dimensional point has no valid pair for thetransformation process, the three-dimensional data encoding device doesnot need to search for the closest three-dimensional point. Therefore,with such a configuration, the three-dimensional data encoding devicedoes not need to search for the closest three-dimensional point for apoint such as a reference point to perform the re-ordering process.Therefore, the three-dimensional data encoding device can immediatelyproceed to process the next three-dimensional point.

FIG. 97 is a block diagram of three-dimensional data encoding device7440 according to this embodiment.

Three-dimensional data encoding device 7440 includes geometryinformation encoder 7441, attribute information encoder 7442, additionalinformation encoder 7443, and multiplexer (MUX) 7444.

Geometry information encoder 7441 encodes geometry information in pointcloud data input to three-dimensional data encoding device 7440.Geometry information encoder 7441 outputs the geometry informationencoded (encoded geometry information) to multiplexer 7444.

Attribute information encoder 7442 encodes attribute information in thepoint cloud data input to three-dimensional data encoding device 7440.Attribute information encoder 7442 outputs the attribute informationencoded (encoded attribute information) to multiplexer 7444.

Additional information encoder 7443 encodes additional information inthe point cloud data input to three-dimensional data encoding device7440. Additional information encoder 7443 outputs the additionalinformation encoded (encoded additional information) to multiplexer7444.

Multiplexer 7444 generates and outputs a bitstream including the encodedgeometry information, the encoded attribute information, and the encodedadditional information. For example, multiplexer 7444 outputs thebitstream to a three-dimensional data decoding device.

FIG. 98 is a block diagram of attribute information encoder 7442according to this embodiment.

Attribute information encoder 7442 includes point cloud re-ordering unit74421, transformer 74422, quantizer 74423, and entropy encoder 74424.

Point cloud re-ordering unit 74421 performs a re-ordering process ofre-ordering the data order of the point cloud data input tothree-dimensional data encoding device 7440. As described above, pointcloud re-ordering unit 74421 re-orders the order of pieces of attributeinformation based on geometry information, for example.

Transformer 74422 performs a transformation process for the attributeinformation in the re-ordered point cloud data.

Quantizer 74423 performs a quantization process on the point cloud datasubjected to the transformation process.

Entropy encoder 74424 performs an entropy-encoding process on thequantized point cloud data.

FIG. 99 is a block diagram of point cloud re-ordering unit 74421according to this embodiment.

Point cloud re-ordering unit 74421 includes Morton ordering unit 744211and re-ordering unit 744212.

Morton ordering unit 744211 re-orders the pieces of attributeinformation in the input point cloud data in a Morton order.

Re-ordering unit 744212 re-orders the point cloud data re-ordered in theMorton order based on geometry information or three-dimensionaldistances as described above.

FIG. 100 is a block diagram of three-dimensional data decoding device7450 according to this embodiment.

Three-dimensional data decoding device 7450 includes demultiplexer(DeMUX) 7451, geometry information decoder 7452, attribute informationdecoder 7453, and additional information decoder 7454.

Demultiplexer 7451 divides the bitstream into the encoded geometryinformation, the encoded attribute information, and the encodedadditional information and outputs the encoded geometry information, theencoded attribute information, and the encoded additional information.Specifically, demultiplexer 7451 outputs the encoded geometryinformation included in the bitstream to geometry information decoder7452, outputs the encoded attribute information included in thebitstream to attribute information decoder 7453, and outputs the encodedadditional information included in the bitstream to additionalinformation decoder 7454.

Geometry information decoder 7452 decodes the encoded geometryinformation to generate geometry information, and outputs the generatedgeometry information.

Attribute information decoder 7453 decodes the encoded attributeinformation to generate attribute information, and outputs the generatedattribute information.

Additional information decoder 7454 decodes the encoded additionalinformation to generate additional information, and outputs thegenerated additional information.

FIG. 101 is a block diagram of attribute information decoder 7453according to this embodiment.

Attribute information decoder 7453 includes entropy decoder 74531,inverse quantizer 74532, point cloud re-ordering unit 74533, and inversetransformer 74534.

Entropy decoder 74531 performs a variable-length decoding of thebitstream. For example, entropy decoder 74531 arithmetically decodes theencoded attribute information to generate a binary signal, and generatesa quantization coefficient from the generated binary signal.

Inverse quantizer 74532 generates an inverse quantization coefficient byinverse-quantizing the quantization coefficient received from entropydecoder 74531 using the quantization parameter added to the bitstream orthe like.

Inverse transformer 74534 inverse-transforms the inverse quantizationcoefficient received from inverse quantizer 74532. For example, inversetransformer 74534 performs a reverse process to the process bytransformer 74422.

In this way, the same point cloud data as the point cloud datare-ordered by three-dimensional data encoding device 7440 is generated.

Point cloud re-ordering unit 74533 re-orders the pieces of attributeinformation in the point cloud data by performing a re-ordering processon the point cloud data generated by inverse transformer 74534. Forexample, point cloud re-ordering unit 74533 performs a reverse processto the process by point cloud re-ordering unit 74421. In this way, pointcloud data in which the pieces of data are arranged in the same order asthose in the point cloud data input to the three-dimensional dataencoding device is generated.

FIG. 102 is a block diagram of three-dimensional data encoding device7460 according to a variation of this embodiment.

Three-dimensional data encoding device 7460 includes geometryinformation encoder 7461, attribute information encoder 7462, additionalinformation encoder 7463, and multiplexer 7464.

Geometry information encoder 7461 encodes geometry information in pointcloud data input to three-dimensional data encoding device 7460.Geometry information encoder 7461 outputs the geometry informationencoded (encoded geometry information) to multiplexer 7464.

Attribute information encoder 7462 encodes attribute information in thepoint cloud data input to three-dimensional data encoding device 7460.Attribute information encoder 7462 outputs the attribute informationencoded (encoded attribute information) to multiplexer 7464.

Attribute information encoder 7462 also modifies the order of pieces ofdata in the point cloud data before encoding the attribute information.Attribute information encoder 7462 generates a re-ordering table (anexample of the swapping information described above) that indicates theorder of the pieces of data yet to be modified (yet to be re-ordered),encodes the generated re-ordering table, and outputs the encodedre-ordering table to multiplexer 7464.

Additional information encoder 7463 encodes additional information inthe point cloud data input to three-dimensional data encoding device7460. Additional information encoder 7463 outputs the additionalinformation encoded (encoded additional information) to multiplexer7464.

Multiplexer 7464 generates and outputs a bitstream including the encodedgeometry information, the encoded attribute information, the encodedre-ordering table, and the encoded additional information. For example,multiplexer 7464 outputs the bitstream to a three-dimensional datadecoding device.

FIG. 103 is a block diagram of attribute information encoder 7462according to a variation of this embodiment.

Attribute information encoder 7462 includes re-ordering table generator74621, transformer 74622, quantizer 74623, and entropy encoder 74624.

Re-ordering table generator 74621 performs a re-ordering process ofre-ordering the data order of the point cloud data input tothree-dimensional data encoding device 7460. As described above,re-ordering table generator 74621 re-orders the order of pieces ofattribute information based on geometry information, for example.Re-ordering table generator 74621 also generates the encoded re-orderingtable described above. Re-ordering table generator 74621 outputs thegenerated encoded re-ordering table to entropy encoder 74624, forexample.

Transformer 74622 performs a transformation process for the attributeinformation in the re-ordered point cloud data.

Quantizer 74623 performs a quantization process on the point cloud datasubjected to the transformation process.

Entropy encoder 74624 performs an entropy-encoding process on thequantized point cloud data. For example, entropy encoder 74624 outputsthe point cloud data subjected to the entropy encoding process and theencoded re-ordering table to multiplexer 7464.

As described above, the three-dimensional data encoding device maygenerate a re-ordering table based on a transformation process and usethe re-ordering table. The three-dimensional data encoding device mayencode the generated re-ordering table and transmit the encodedre-ordering table to the three-dimensional data decoding device.

In this way, the three-dimensional data decoding device can more quicklyperform the decoding process.

FIG. 104 is a block diagram of three-dimensional data decoding device7470 according to a variation of this embodiment.

Three-dimensional data decoding device 7470 includes demultiplexer 7471,geometry information decoder 7472, attribute information decoder 7473,and additional information decoder 7474.

Demultiplexer 7471 divides the bitstream into the encoded geometryinformation, the encoded attribute information, the encoded re-orderingtable, and the encoded additional information and outputs the encodedgeometry information, the encoded attribute information, the encodedre-ordering table, and the encoded additional information. Specifically,demultiplexer 7471 outputs the encoded geometry information included inthe bitstream to geometry information decoder 7472, outputs the encodedattribute information and the encoded re-ordering table included in thebitstream to attribute information decoder 7473, and outputs the encodedadditional information included in the bitstream to additionalinformation decoder 7474.

Geometry information decoder 7472 decodes the encoded geometryinformation to generate geometry information, and outputs the generatedgeometry information.

Attribute information decoder 7473 decodes the encoded re-ordering tableto generate a re-ordering table. Attribute information decoder 7473 alsodecodes the encoded attribute information to generate attributeinformation, and outputs the generated attribute information.

Additional information decoder 7474 decodes the encoded additionalinformation to generate additional information, and outputs thegenerated additional information.

FIG. 105 is a block diagram of attribute information decoder 7473according to a variation of this embodiment.

Attribute information decoder 7473 includes entropy decoder 74731,inverse quantizer 74732, and inverse transformer 74733.

Entropy decoder 74731 performs a variable-length decoding of thebitstream. For example, entropy decoder 74731 arithmetically decodes theencoded attribute information to generate a binary signal, and generatesa quantization coefficient from the generated binary signal. Entropydecoder 74731 also decodes the encoded re-ordering table to generate are-ordering table, and outputs the generated re-ordering table toinverse transformer 74733.

Inverse quantizer 74732 generates an inverse quantization coefficient byinverse-quantizing the quantization coefficient received from entropydecoder 74731 using the quantization parameter added to the bitstream orthe like.

Inverse transformer 74733 inverse-transforms the inverse quantizationcoefficient received from inverse quantizer 74732. For example, inversetransformer 74733 performs a reverse process to the process bytransformer 74622.

In this way, the same point cloud data as the point cloud datare-ordered by three-dimensional data encoding device 7440 is generated.Inverse transformer 74733 re-orders the pieces of attribute informationin the point cloud data by performing a re-ordering process on the pointcloud data generated by the inverse transformation process based on there-ordering table.

In this way, point cloud data in which the pieces of data are arrangedin the same order as those in the point cloud data input to thethree-dimensional data encoding device is generated.

FIG. 106 is a flowchart of a three-dimensional data encoding processaccording to this embodiment.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S7401). For example, the three-dimensional dataencoding device performs the encoding using an octree representation.

The three-dimensional data encoding device then performs atransformation process on attribute information (S7402). For example,after the encoding of geometry information, if the position of athree-dimensional point is changed because of quantization or the like,the three-dimensional data encoding device reassigns the attributeinformation on the original three-dimensional point to thethree-dimensional point changed in position.

Note that the three-dimensional data encoding device may perform thereassignment by interpolation of values of the attribute informationaccording to the amount of change in position. For example, thethree-dimensional data encoding device may detect N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, take aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determine theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation subjected to the transformation process (S7403).

Note that, when the three-dimensional data encoding device encodes aplurality of pieces of attribute information, the three-dimensional dataencoding device may sequentially encode the plurality of pieces ofattribute information. For example, when the three-dimensional dataencoding device encodes color and reflectance as attribute information,the three-dimensional data encoding device may generate a bitstreamincluding the result of encoding of color followed by the result ofencoding of reflectance.

Note that the order of the results of encoding of the attributeinformation added to the bitstream is not limited to the order describedabove, and can be any order.

The three-dimensional data encoding device may add a starting point ofthe encoded data of each attribute information in the bitstream to theheader or the like.

In this way, the three-dimensional data decoding device can selectivelydecode attribute information that needs to be decoded, and therefore canomit the decoding process for attribute information that does not needto be decoded. Therefore, the processing amount of the three-dimensionaldata decoding device can be reduced.

The three-dimensional data encoding device may encode a plurality ofpieces of attribute information in parallel, and integrate the resultsof the encoding into one bitstream.

In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 107 is a flowchart of the attribute information encoding process(S7403) according to this embodiment.

First, the three-dimensional data encoding device performs a re-orderingprocess on the attribute information in the input point cloud data(S7411).

The three-dimensional data encoding device then generates a codingcoefficient from the attribute information by Haar transformation asdescribed above, for example, and applies quantization to the generatedcoding coefficient (S7412). That is, the three-dimensional data encodingdevice generates a coding coefficient for the point cloud datare-ordered by the re-ordering process, and performs a quantizationprocess on the generated coding coefficient.

The three-dimensional data encoding device then encodes the quantizedcoding coefficient to generate encoded attribute information (S7413).

The three-dimensional data encoding device then applies inversequantization to the quantized coding coefficient (S7414).

The three-dimensional data encoding device then applies inverse Haartransformation to the inverse-quantized coding coefficient to decodeattribute information (S7415). For example, the decoded attributeinformation is referred to in the subsequent encoding.

FIG. 108 is a flowchart of the attribute information re-ordering process(S7411) according to this embodiment.

First, the three-dimensional data encoding device re-orders thethree-dimensional points in the input point cloud data in a Mortonorder, and assigns layer 0 to the three-dimensional points (S7421).

The three-dimensional data encoding device then sets i=0 (S7422). Thethree-dimensional data encoding device then determines athree-dimensional point (neighboring three-dimensional point)neighboring to each three-dimensional point in layer i, and performs there-ordering process on each three-dimensional point so that thetransformation process can be applied to the three-dimensional point andthe neighboring three-dimensional point. Alternatively, thethree-dimensional data encoding device performs the swapping process ofswapping the attribute values (values indicated by the attributeinformation) of the three-dimensional points (S7423).

For example, the three-dimensional data encoding device may re-orderthree-dimensional points or swap only attribute values indicated by theattribute information on three-dimensional points in the mannerdescribed above.

Note that threshold a may be provided in advance. In that case, forexample, the three-dimensional data encoding device may apply there-ordering process or swapping process when i<α, and does not need toapply the re-ordering process or swapping process when i is equal to orgreater than a. For example, by setting α=1 in advance, thethree-dimensional data encoding device may be configured to perform there-ordering process or swapping process for layer 0.

In this way, the three-dimensional data encoding device can reduce theprocessing amount.

Note that the three-dimensional data encoding device may add the valueof a to the header or the like of the bitstream.

In this way, the three-dimensional data decoding device can determine upto which layer the re-ordering process or swapping process is to beperformed based on a added to the header or the like, and therefore canproperly decode the bitstream.

The three-dimensional data encoding device then calculates a highfrequency component and a low frequency component by applying thetransformation process to the attribute values of the three-dimensionalpoints assigned to layer i, designates the calculated high frequencycomponent as a coding coefficient, and sets the calculated low frequencycomponent to be a value for layer i+1 (S7424).

The three-dimensional data encoding device then sets i=i+1 (S7425).

The three-dimensional data encoding device then determines whether thenumber of three-dimensional points in layer i is 1 or not (S7426).

When the three-dimensional data encoding device determines that thenumber of three-dimensional points in layer i is not 1 (if No in S7426),the three-dimensional data encoding device returns the process to stepS7423.

On the other hand, when the three-dimensional data encoding devicedetermines that the number of three-dimensional points in layer i is 1if Yes in S7426), the three-dimensional data encoding device sets thevalue of the three-dimensional point in layer i to be coding coefficient(S7427).

Note that, although an example has been shown above in which thethree-dimensional data encoding device repeats the loop (S7423 to S7426)until the number of the three-dimensional points in layer i becomes 1,the present disclosure is not necessarily limited thereto. For example,threshold 13 may be provided in advance. In that case, thethree-dimensional data encoding device may repeat the loop until thenumber of the three-dimensional points in layer i equals to β.

In this way, the three-dimensional data encoding device can reduce theprocessing amount.

Note that the three-dimensional data encoding device may add the valueof β to the header or the like of the bitstream.

In this way, the three-dimensional data decoding device can determine upto which layer the transformation process is to be performed based on βadded to the header or the like, and therefore can properly decode thebitstream.

FIG. 109 is a flowchart of a three-dimensional data decoding deviceaccording to this embodiment.

First, the three-dimensional data decoding device decodes geometryinformation (geometry) from the bitstream (S7431). For example, thethree-dimensional data decoding device performs the decoding using anoctree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7432). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.

Note that the three-dimensional data decoding device can decode theresults of encoding of attribute information in the bitstream in anyorder.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike.

In this way, the three-dimensional data decoding device can selectivelydecode attribute information that needs to be decoded, and therefore canomit the decoding process for attribute information that does not needto be decoded. Therefore, the processing amount of the three-dimensionaldata decoding device can be reduced.

The three-dimensional data decoding device may decode a plurality ofpieces of attribute information in parallel, and integrate the resultsof the decoding into one three-dimensional point cloud.

In this way, the three-dimensional data decoding device can decode aplurality of pieces of attribute information at a high speed.

FIG. 110 is a flowchart of the attribute information decoding process(S7432) according to this embodiment.

First, the three-dimensional data decoding device decodes the codingcoefficient from the bitstream (S7441).

The three-dimensional data decoding device then applies inversequantization to the coding coefficient (S7442).

The three-dimensional data decoding device then applies inverse Haartransformation to the inverse-quantized coding coefficient to decode theattribute information, and performs the re-ordering process on thedecoded attribute information (S7443).

FIG. 111 is a flowchart of the attribute information re-ordering process(S7443) according to this embodiment.

First, the three-dimensional data decoding device sets i=N (S7451).Here, N represents the number of layers, and is calculated based on thegeometry information on the three-dimensional points included in thepoint cloud data, for example.

The three-dimensional data decoding device then applies an inversetransformation process to the coding coefficient for layer i toreproduce the values of three-dimensional points in layer i (S7452).

The three-dimensional data decoding device then determines a neighboringthree-dimensional point of each three-dimensional point in layer i,generates the re-ordering information or swapping information, andperforms the re-ordering process of recovering the original positions ofthe attribute values that have been re-ordered or swapped by thethree-dimensional data encoding device (S7453).

For example, the three-dimensional data decoding device generates there-ordering information or the swapping information on the attributevalues indicated by the attribute information in the manner describedabove.

Note that threshold α may be provided in advance. In that case, thethree-dimensional data decoding device may generate the re-orderinginformation or swapping information when i<α, and does not need togenerate re-ordering information or swapping information when i is equalto or greater than α.

For example, by setting α=1 in advance, the three-dimensional datadecoding device may generate the re-ordering information or swappinginformation for layer 0.

In this way, the three-dimensional data decoding device can reduce theprocessing amount.

Note that the three-dimensional data decoding device may decode andobtain the value of a added to the header or the like of the bitstream.

The three-dimensional data decoding device then sets i=i−1 (S7454).

The three-dimensional data decoding device then determines whether layeri is the lowermost layer or not (S7455).

When the three-dimensional data decoding device determines that layer iis not the lowermost layer (if No in S7455), the three-dimensional datadecoding device returns the process to step S7452.

On the other hand, when the three-dimensional data decoding devicedetermines that layer i is the lowermost layer (if Yes in S7455), thethree-dimensional data decoding device outputs the value of thethree-dimensional point in layer i as a decoded value (that is, anattribute value) (S7456).

Next, a three-dimensional data encoding device and a three-dimensionaldata decoding device according to a variation of this embodiment will bedescribed.

Although an example has been described above in which thethree-dimensional data encoding device uses distance information orgeometry information on three-dimensional points to re-order thethree-dimensional points or swaps pieces of attribute information on thethree-dimensional points before the transformation process, the presentdisclosure is not necessarily limited thereto.

For example, the point cloud data input to the three-dimensional dataencoding device may include information that indicates whether toperform the re-ordering process or not or information that indicateswhether to perform the swapping process or not added to the header orthe like thereof. In that case, the three-dimensional data encodingdevice may determine and choose whether to perform the re-orderingprocess or not or whether to perform the swapping process or not basedon the information.

Specifically, when giving priority to improving the coding efficiency,for example, the three-dimensional data encoding device performs there-ordering process or swapping process before the transformationprocess, adds a flag that indicates that the re-ordering process orswapping process has been performed to the header or the like, and turnon the flag.

On the other hand, when giving priority to reducing the processingamount, the three-dimensional data encoding device performs thetransformation process without performing the re-ordering process orswapping process, adds the flag described above to the header or thelike, and turn off the flag.

In that case, for example, the three-dimensional data decoding devicedecodes the flag described above from the header of the receivedbitstream. And the three-dimensional data decoding device performs there-ordering process or swapping process after performing the inversetransformation process if the flag is on, and does not perform there-ordering process or swapping process if the flag is off.

In this way, providing the flag in the bitstream allows thethree-dimensional data decoding device to properly determine whether thethree-dimensional data encoding device has given priority to improvingthe coding efficiency or reducing the processing amount.

Although an example has been described in which the three-dimensionaldata encoding device adds the re-ordering information or swappinginformation to the header or the like, the present disclosure is notnecessarily limited thereto. For example, the three-dimensional dataencoding device may encode the re-ordering information or swappinginformation as new attribute information (attribute) on thethree-dimensional points. Specifically, the three-dimensional dataencoding device encodes, as attribute information, ordering informationon the three-dimensional points yet to be subjected to the re-orderingprocess or swapping process. The three-dimensional data decoding devicecan recognize the ordering information on the three-dimensional pointsyet to be subjected to the re-ordering process or swapping process bydecoding the ordering information, which is attribute information, andtherefore can recover the original ordering of the three-dimensionalpoints in the point cloud data having been subjected to the re-orderingprocess or swapping process.

Embodiment 10

When encoding attribute information on a three-dimensional pointincluded in point cloud data to be encoded using Region AdaptiveHierarchical Transform (RAHT), the three-dimensional data encodingdevice may encode the attribute information without applying a RAHTprocess if the number of the input three-dimensional points is 1.

FIG. 112 is a flowchart of a three-dimensional data encoding methodaccording to this embodiment.

The three-dimensional data encoding device determines whether or not thenumber of the input three-dimensional points is greater than 1 (S7801).Note that the input three-dimensional points are the three-dimensionalpoints included in the point cloud data to be encoded. Here, thethree-dimensional points included in the point cloud data to be encodedare the three-dimensional points to be encoded on an encoding unitbasis. The encoding unit is a slice or a tile, for example.

When the number of the input three-dimensional points is greater than 1(if Yes in S7801), the three-dimensional data encoding device generatesa RAHT hierarchical structure, such as that shown in FIG. 113, for theplurality of three-dimensional points, and calculates an encodingcoefficient as described above in Embodiment 7 with reference to FIG. 30or the like (S7802). Specifically, the three-dimensional data encodingdevice calculates Haar-transformed encoding coefficients Ta1, Ta5, Tb1,Tb3, Tc1, and d0 by applying a Haar transform on two pieces of attributeinformation that are adjacent in the order of Morton codes among aplurality of pieces of attribute information a0, a1, a2, a3, a4, and a5on a plurality of input three-dimensional points to generate attributeinformation in an upper layer and repeating such generation. Thethree-dimensional data encoding device then generates a bitstreamincluding the calculated encoding coefficients.

On the other hand, when the number of the input three-dimensional pointsis 1 (if No in S7801), the three-dimensional data encoding device skipsthe step of generating a RAHT hierarchical structure, quantizes a valueof attribute information on the three-dimensional point as an encodingcoefficient, and encodes the quantized value by applying binaryarithmetic encoding (S7803). For example, the three-dimensional dataencoding device generates a bitstream including attribute information a0on the one three-dimensional point as an encoding coefficient.

In this way, the three-dimensional data encoding device can properlyencode the three-dimensional point even when the number of the inputthree-dimensional points is 1.

Note that the three-dimensional data encoding device may include, in thebitstream, information EnableRAHT=1 that indicates whether to use a RAHThierarchical structure to encode attribute information onthree-dimensional points or not by adding the information to the headerof the bitstream. This allows the three-dimensional data decoding deviceto determine whether RAHT should be used for decoding by decodingEnableRAHT in the header, and thus to properly decode the bitstream.Note that when EnableRAHT=0, attribute information on three-dimensionalpoints included in point cloud data to be encoded may be encoded ordecoded in a method that involves predicting attribute information onthree-dimensional points to be encoded by generating a LoD hierarchicalstructure.

Note that when attribute information is N-dimensional, thethree-dimensional data encoding device may independently apply a Haartransform for each dimension and calculate an encoding coefficient foreach dimension. If attribute information is color information (RGB orYUV, for example), the three-dimensional data encoding device may applya Haar transform for each component of the color information andcalculate an encoding coefficient for each component.

The three-dimensional data encoding device may apply a Haar transform tolayers L, L+1, . . . , and Lmax in the listed order. In that case, thecloser to layer Lmax the encoding coefficient, the more the lowerfrequency components of the input attribute information included in theencoding coefficient is.

When decoding a bitstream encoded using RAHT, the three-dimensional datadecoding device reconstructs attribute information on athree-dimensional point by applying an inverse Haar transform on aninverse-quantized encoding coefficient. In this case, when the number ofthe three-dimensional points to be decoded obtained from the bitstreamis 1, the three-dimensional data decoding device may decode theattribute information on the three-dimensional point to be decodedwithout applying an inverse Haar transform of RAHT.

FIG. 114 is a flowchart of a three-dimensional data decoding methodaccording to this embodiment.

The three-dimensional data decoding device obtains one or morethree-dimensional points and an encoding coefficient from a bitstream(S7811).

The three-dimensional data decoding device determines whether or not thenumber of the three-dimensional points to be decoded obtained from thebitstream is greater than 1 (S7812). Note that the number of thethree-dimensional points to be decoded is the number of thethree-dimensional points to be decoded on a decoding unit basis. Thedecoding unit is a slice or a tile, for example.

When the number of the three-dimensional points to be decoded obtainedfrom the bitstream is greater than 1 of Yes in S7812), thethree-dimensional data decoding device generates a RAHT hierarchicalstructure, such as that shown in FIG. 115, for the plurality ofthree-dimensional points, and reconstructs attribute information asdescribed above in Embodiment 7 with reference to FIG. 31 or the like(S7813). Specifically, the three-dimensional data decoding deviceobtains inverse-quantized encoding coefficients Ta1, Ta5, Tb1, Tb3, Tc1,and d0, and reconstructs a plurality of pieces of attribute informationa0, a1, a2, a3, a4, and a5 on a plurality of input three-dimensionalpoints by applying an inverse Haar transform on encoding coefficientsTa1, Ta5, Tb1, Tb3, Tc1, and d0 using the generated RAHT hierarchicalstructure.

On the other hand, when the number of the three-dimensional points to bedecoded obtained from the bitstream is 1 (if No in S7812), thethree-dimensional data decoding device skips the step of generating aRAHT hierarchical structure, and reconstructs attribute information onthe three-dimensional point by using an inverse-quantized encodingcoefficient as a value of the attribute information on thethree-dimensional point (S7814). For example, the three-dimensional datadecoding device reconstructs attribute information by using an encodingcoefficient included in the bitstream as attribute information a0.

In this way, the three-dimensional data decoding device can properlydecode the three-dimensional point even when the number of thethree-dimensional points to be decoded is 1.

Note that when attribute information is N-dimensional, thethree-dimensional data decoding device may independently apply aninverse Haar transform for each dimension and decode an encodingcoefficient for each dimension. If attribute information is colorinformation (RGB or YUV, for example), for example, thethree-dimensional data decoding device may apply an inverse Haartransform to an encoding coefficient for each component of the colorinformation and decode an attribute value for each component.

FIG. 116 is a flowchart of a three-dimensional data encoding processaccording to this embodiment. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7821). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7822). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7823). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. This allows thethree-dimensional data decoding device to selectively decode attributeinformation that needs to be decoded and therefore omit the decodingprocess for attribute information that does not need to be decoded.Therefore, the processing amount of the three-dimensional data decodingdevice can be reduced. The three-dimensional data encoding device mayencode a plurality of pieces of attribute information in parallel, andintegrate the results of the encoding into one bitstream. In this way,the three-dimensional data encoding device can encode a plurality ofpieces of attribute information at a high speed.

FIG. 117 is a flowchart of an attribute information encoding process(S7823). First, the three-dimensional data encoding device generates anencoding coefficient from attribute information by Haar transform(S7831). Specifically, in step S7831, the three-dimensional dataencoding device performs the series of steps S7801 to S7803 describedabove with reference to FIG. 112. The three-dimensional data encodingdevice then applies quantization to the encoding coefficient (S7832).The three-dimensional data encoding device then encodes the quantizedencoding coefficient to generate encoded attribute information(bitstream) (S7833).

The three-dimensional data encoding device also applies an inversequantization to the quantized encoding coefficient (S7834). Thethree-dimensional data decoding device then decodes the attributeinformation by applying an inverse Haar transform to theinverse-quantized encoding coefficient (S7835). For example, the decodedattribute information is to be referred to in a subsequent encoding.

FIG. 118 is a flowchart of a three-dimensional data decoding processaccording to this embodiment. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7841). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7842). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 119 is a flowchart of an attribute information decoding process(S7842). First, the three-dimensional data decoding device decodes anencoding coefficient from a bitstream (S7851). The three-dimensionaldata decoding device then applies an inverse quantization to theencoding coefficient (S7852). The three-dimensional data decoding devicethen decodes attribute information by applying an inverse Haar transformto the inverse-quantized encoding coefficient (S7853). Specifically, instep S7853, the three-dimensional data decoding device performs theseries of steps S7811 to S7814 described above with reference to FIG.114.

FIG. 120 is a block diagram of attribute information encoder 7800included in the three-dimensional data encoding device. Attributeinformation encoder 7800 includes sorter 7801, Haar transformer 7802,quantizer 7803, inverse quantizer 7804, inverse Haar transformer 7805,memory 7806, and arithmetic encoder 7807.

Sorter 7801 generates a Morton code using geometry information on athree-dimensional point, and sorts a plurality of three-dimensionalpoints in the order of Morton codes. Haar transformer 7802 generates anencoding coefficient by applying a Haar transform to attributeinformation. Specifically, Haar transformer 7802 performs the series ofsteps S7801 to S7803 described above with reference to FIG. 112.Quantizer 7803 quantizes the encoding coefficient of the attributeinformation.

Inverse quantizer 7804 inverse-quantizes the quantized encodingcoefficient. Inverse Haar transformer 7805 applies an inverse Haartransform to the encoding coefficient. Specifically, inverse Haartransformer 7805 performs a process similar to the series of steps S7811to S7814 described above with reference to FIG. 114. Memory 7806 storesvalues of the decoded attribute information on the plurality ofthree-dimensional points. For example, the decoded attribute informationon the three-dimensional points stored in memory 7806 may be used forprediction of a three-dimensional point yet to be encoded.

Arithmetic encoder 7807 calculates ZeroCnt from the quantized encodingcoefficient, and arithmetically encodes ZeroCnt. Arithmetic encoder 7807also arithmetically encodes any quantized encoding coefficient that isnot zero. Arithmetic encoder 7807 may binarize the encoding coefficientbefore the arithmetic encoding. Arithmetic encoder 7807 may generate andencode various kinds of header information.

FIG. 121 is a block diagram of attribute information decoder 7810included in the three-dimensional data decoding device. Attributeinformation decoder 7810 includes arithmetic decoder 7811, inversequantizer 7812, inverse Haar transformer 7813, and memory 7814.

Arithmetic decoder 7811 arithmetically decodes ZeroCnt and the encodingcoefficient included in the bitstream. Note that arithmetic decoder 7811may decode various kinds of header information.

Inverse quantizer 7812 inverse-quantizes the arithmetically decodedencoding coefficient. Inverse Haar transformer 7813 applies an inverseHaar transform to the inverse-quantized encoding coefficient.Specifically, inverse Haar transformer 7813 performs the series of stepsS7811 to S7814 described above with reference to FIG. 114. Memory 7814stores values of the decoded attribute information on the plurality ofthree-dimensional points. For example, the decoded attribute informationon the three-dimensional points stored in memory 7814 may be used forprediction of a three-dimensional point yet to be decoded.

Note that, although an example in which three-dimensional points areencoded in the order from bottom layer to top layer has been describedin the above embodiment, the present disclosure is not necessarilylimited thereto. For example, Haar-transformed encoding coefficients maybe scanned in the order from top layer to bottom layer. Note that, inthat case, the three-dimensional data encoding device may encode thenumber of successive values of 0 as ZeroCnt.

The three-dimensional data encoding device may change whether to use theencoding method using ZeroCnt described in this embodiment on a WLD, SPCor volume basis. In that case, the three-dimensional data encodingdevice may add, to the header information, information that indicateswhether the encoding method using ZeroCnt has been applied or not. Thisallows the three-dimensional data decoding device to properly performdecoding. In an example of such changing, the three-dimensional dataencoding device counts the number of occurrences of an encodingcoefficient having a value of 0 for one volume. The three-dimensionaldata encoding device applies the method using ZeroCnt to the next volumeif the count is greater than a predetermined threshold, and does notapply the method using ZeroCnt to the next volume if the count issmaller than or equal to the threshold. In this way, thethree-dimensional data encoding device can appropriately change whetherto apply the encoding method using ZeroCnt according to thecharacteristics of the three-dimensional point to be encoded, so thatthe encoding efficiency can be improved.

Although an example has been shown above in which the three-dimensionaldata encoding device or the three-dimensional data decoding deviceaccording to this embodiment skips the RAHT process if the number of theinput three-dimensional points or the number of the three-dimensionalpoints to be decoded is 1, the present disclosure is not necessarilylimited thereto. The three-dimensional data encoding device or thethree-dimensional data decoding device may define threshold a (arepresents an integer greater than or equal to 1), and calculate anencoding coefficient by performing the RAHT process if the number of theinput three-dimensional points or the number of the three-dimensionalpoints to be decoded is greater than α, and skip the RAHT process if thenumber of the input three-dimensional points or the number of thethree-dimensional points to be decoded is smaller than or equal to α.Threshold α is an example predetermined number. By performing theencoding or decoding process by skipping the RAHT process when thenumber of the three-dimensional points to be encoded is smaller than orequal to threshold α in this way, the processing time can be reduced,and the cc or less three-dimensional points can be appropriately encodedor decoded. Note that threshold α may be added to the header or the likeor may be prescribed by a profile, a level or the like of a standard.

Although an example has been shown above in which the three-dimensionaldata encoding device or the three-dimensional data decoding deviceaccording to this embodiment skips the RAHT process if the number of theinput three-dimensional points or the number of the three-dimensionalpoints to be decoded is 1, the present disclosure is not necessarilylimited thereto. The three-dimensional data encoding device or thethree-dimensional data decoding device may be inhibited from using RAHTfor encoding or decoding when the number of the input three-dimensionalpoints or the number of the three-dimensional points to be decoded is 1.Specifically, when the number of the input three-dimensional points orthe number of the three-dimensional points to be decoded is 1, thethree-dimensional data encoding device or the three-dimensional datadecoding device may avoid using RAHT for encoding or decoding bylimiting the value of EnableRAHT to 0. In this way, thethree-dimensional data encoding device or the three-dimensional datadecoding device can appropriately encode or decode one three-dimensionalpoint.

Note that EnableRAHT, which is information used to change whether toencode attribute information on a three-dimensional point by applyingRAHT or not, may be added to the header on a processing unit basis, inorder that the process is changed on a basis of a processing unit, suchas WLD, SPC, or volume. EnableRAHT may be added on a slice basis. Forexample, when EnableRAHT is added to a slice header, thethree-dimensional points in the slice may be encoded by settingEnableRAHT=0 when the number of the three-dimensional points in theslice is smaller than or equal to a certain number, such as when thenumber is 1, and the three-dimensional points in the slice may beencoded by setting EnableRAHT=1 when the number of the three-dimensionalpoints in the slice is greater than the certain number, such as when thenumber is greater than or equal to 2. In this way, three-dimensionalpoints can be appropriately encoded by changing whether to apply RAHT ornot based on the number of the three-dimensional points in the slice.

Note that an example in which whether to apply RAHT or not is changedbased on the number of the three-dimensional points in the slice hasbeen shown above, the present disclosure is not necessarily limitedthereto, and whether to apply RAHT or not may be changed based onwhether the three-dimensional points in the slice is a dense point cloudor a sparse point cloud. Here, the “dense point cloud” may refer to apoint cloud in which the distances between the three-dimensional pointsare relatively short (that is, smaller than a predetermined distance),and the three-dimensional points are located at relatively equaldistances from each other. The “sparse point cloud” may refer to a pointcloud in which the distances between the three-dimensional points arerelatively long (that is, greater than the predetermined distance), andthe three-dimensional points are located at relatively random distancesfrom each other. As a specific example of the changing, thethree-dimensional data encoding device may encode attribute informationby setting EnableRAHT=0 and EnableUnuniformLoD=0, calculatinginformation on distances between the input three-dimensional points inthe method according to Embodiment 8 (described above with reference toFIG. 50) to generate LoD layers when the three-dimensional points in theslice is a dense point cloud, for example, and may encode attributeinformation by applying RAHT by setting EnableRAHT=1 otherwise, such aswhen the three-dimensional points in the slice is a sparse point cloud.In this way, the three-dimensional data encoding device can change theattribute information encoding method based on whether the inputthree-dimensional points or the three-dimensional points in a slice area dense point cloud or not, so that the encoding efficiency can beimproved.

Although an example has been shown above in which the three-dimensionaldata encoding device according to this embodiment skips the Haartransform process of RAHT and generates a value of attribute informationon a three-dimensional point as an encoding coefficient when the numberof the input three-dimensional points is 1, the present disclosure isnot necessarily limited to cases where RAHT is used, and can be appliedto a method of calculating a predicted value by generating a LoD, forexample. Specifically, when the number of the input three-dimensionalpoints is 1, the three-dimensional data encoding device may skip aprocess of generating a LoD and calculating a predicted value, andencode a value of attribute information on the three-dimensional pointby quantizing an encoding coefficient used as the value of the attributeinformation on the three-dimensional point and applying a binaryarithmetic encoding on the quantized encoding coefficient. In this way,when the number of the input three-dimensional points is 1, thethree-dimensional point can be properly encoded while reducing theprocessing amount. Similarly, when the number of the three-dimensionalpoints to be decoded is 1, the three-dimensional data decoding devicemay skip a process of generating a LoD and calculating a predictedvalue, and decode a value of attribute information on thethree-dimensional point by using inverse-quantized encoding coefficientas the value of the attribute information on the three-dimensionalpoint. In this way, when the number of the three-dimensional points tobe decoded is 1, the three-dimensional point can be properly decodedwhile reducing the processing amount.

Embodiment 11

In this embodiment, a reversible (Lossless) attribute encoding will bedescribed. To achieve high compression, attribute information includedin Point Cloud Compression (PCC) data is transformed in a plurality ofmethods, such as Lifting, Region Adaptive Hierarchical Transform (RAHT)and other transform methods. Here, Lifting is one of transform methodsusing Level of Detail (LoD).

Important signal information tends to be included in a low-frequencycomponent, and therefore the code amount is reduced by quantizing ahigh-frequency component. That is, the transform process has strongenergy compression characteristics.

On the other hand, in order to maintain the original information whilereducing the number of bits, the reversible compression is needed.Existing transforms, such as Lifting and RAHT, involves a division and asquare-root operator and therefore cannot achieve reversiblecompression. In order to achieve an efficient and effective reversiblecompression, an integer-to-integer transform that is not complicated isneeded.

FIG. 122 is a diagram showing a configuration of a three-dimensionaldata encoding device. As shown in FIG. 122, the three-dimensional dataencoding device includes integer transformer 8301 and entropy encoder8302. Integer transformer 8301 generates a coefficient value byinteger-transforming input point cloud data. Entropy encoder 8302generates a bitstream by entropy-encoding the coefficient value.

FIG. 123 is a diagram showing a configuration of a three-dimensionaldata decoding device. As shown in FIG. 123, the three-dimensional datadecoding device includes entropy decoder 8303 and inverse integertransformer 8304. Entropy decoder 8303 obtains a coefficient value bydecoding a bitstream. Inverse integer transformer 8304 generates outputpoint cloud data by inverse integer-transforming the coefficient value.

In the following, RAHT will be describe. RAHT is an example of thetransform processings applied to three-dimensional points. FIG. 124 is adiagram for illustrating RAHT. m-th Low-frequency component L_(l, m) andm-th high-frequency component H_(l, m) in layer 1 are expressed by twolow-frequency components C_(l+1, 2m) and C_(1+1, 2m+1) in layer 1+1according to the following (Equation O1). That is, low-frequencycomponent L_(l, m) is expressed by (Equation O2), and high-frequencycomponent H_(l, m) is expressed by (Equation O3).

A high-frequency component is encoded by quantization and entropyencoding. A low-frequency component is used in the subsequent layer asshown by (Equation O4). Coefficient α and β are updated for each upperlayer. Coefficients α and β are expressed by (Equation O5) and (EquationO6), respectively. Weight W_(l, m) is expressed by (Equation O7).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 12} \right\rbrack & \; \\{\begin{bmatrix}L_{l,m} \\H_{l,m}\end{bmatrix} = {\begin{bmatrix}\alpha & \beta \\{- \beta} & \alpha\end{bmatrix}\begin{bmatrix}C_{{l + 1},{2m}} \\C_{{l + 1},{{2m} + 1}}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O1}} \right) \\{L_{l,m} = {{\alpha\; C_{{l + 1},{2m}}} + {\beta\; C_{{l + 1},{{2m} + 1}}}}} & \left( {{Equation}\mspace{14mu}{O2}} \right) \\{H_{l,m} = {{\alpha\; C_{{l + 1},{{2m} + 1}}} - {\beta\; C_{{l + 1},{2m}}}}} & \left( {{Equation}\mspace{14mu}{O3}} \right) \\{C_{l,m} = L_{l,m}} & \left( {{Equation}\mspace{11mu}{O4}} \right) \\{\alpha = \frac{\sqrt{w_{{l + 1},{2m}}}}{\sqrt{w_{{l + 1},{2m}} + w_{{l + 1},{{2m} + 1}}}}} & \left( {{Equation}\mspace{14mu}{O5}} \right) \\{\beta = \frac{\sqrt{w_{{l + 1},{{2m} + 1}}}}{\sqrt{w_{{l + 1},{2m}} + w_{{l + 1},{{2m} + 1}}}}} & \left( {{Equation}\mspace{14mu}{O6}} \right) \\{w_{l,m} = {w_{{l + 1},{2m}} + w_{{l + 1},{{2m} + 1}}}} & \left( {{Equation}\mspace{14mu}{O7}} \right)\end{matrix}$

Next, an integer-to-integer transform will be described. The RAHTprocess involves a square-root operator and a division. This means thatinformation is lost in RAHT, and RAHT cannot achieve reversiblecompression. On the other hand, the integer-to-integer transform canachieve reversible compression.

FIG. 125 is a diagram for illustrating an integer-to-integer transform.In the integer-to-integer transform, a fixed value is used as acoefficient in RAHT. For example, an unnormalized Haar transformexpressed by the following (Equation O8) is used. That is, low-frequencycomponent L_(l, m) is expressed by (Equation O9), and high-frequencycomponent H_(l, m) is expressed by (Equation O10).

A high-frequency component is encoded by quantization and entropyencoding. A low-frequency component is used in the subsequent layer asshown by (Equation O11).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 13} \right\rbrack & \; \\{\begin{bmatrix}L_{l,m} \\H_{l,m}\end{bmatrix} = {\begin{bmatrix}\frac{1}{2} & \frac{1}{2} \\{- 1} & 1\end{bmatrix}\begin{bmatrix}C_{{l + 1},{2m}} \\C_{{l + 1},{{2m} + 1}}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O8}} \right) \\{L_{l,m} = {\left( {C_{{l + 1},{2m}} + C_{{l + 1},{{2m} + 1}}} \right)/2}} & \left( {{Equation}\mspace{14mu}{O9}} \right) \\{H_{l,m} = {C_{{l + 1},{{2m} + 1}} - C_{{l + 1},{2m}}}} & \left( {{Equation}\mspace{14mu}{O10}} \right) \\{C_{l,m} = L_{l,m}} & \left( {{Equation}\mspace{14mu}{O11}} \right)\end{matrix}$

The unnormalized Haar transform can be rewritten as (Equation O12) and(Equation O13).

$\begin{matrix}{\left\lbrack {{Math}.\mspace{14mu} 14} \right\rbrack\;} & \; \\{H_{l,m} = {C_{{l + 1},{{2m} + 1}} - C_{{l + 1},{2m}}}} & \left( {{Equation}\mspace{14mu}{O12}} \right) \\\begin{matrix}{L_{l,m} = \frac{C_{{l + 1},{2m}} + C_{{l + 1},{{2m} + 1}}}{2}} \\{= {C_{{l + 1},{2m}} + \frac{H_{l,m}}{2}}}\end{matrix} & \left( {{Equation}\mspace{14mu}{O13}} \right)\end{matrix}$

An integer Haar transform is achieved according to (Equation O14) and(Equation O15), and an inverse integer Haar transform is achievedaccording to (Equation O16) and (Equation O17). Here, ∥ represents afloor function. Since both (Equation O15) and (Equation O16) includes|H_(l, m)/2|, a loss caused by |H_(l, m)/2| is cancelled by the integerHaar transform and the inverse integer Haar transform. In this way,reversible compression is achieved. Here, C_(i, j), is defined as aninteger, and therefore, H_(i, j) and L_(i, j) are also integers.

[Math. 15]

H _(l,m) =C _(l+1,2m+1) −C _(l+1,2m)  (Equation O14)

L _(l,m) =C _(l+1,2m) +└H _(l,m)/2┘  (Equation O15)

C _(l,2m) =L _(l,m) −└H _(l,m)/2┘  (Equation O16)

C _(l+1,2m+1) =H _(l+m) −C _(l+1,2m)  (Equation O17)

Therefore, an efficient implementation can be achieved by the following(Equation O18) to (Equation O21). That is, a transform can be achievedby one addition, one subtraction, and one right shifting (downshifting).

[Math. 16]

H _(l,m) =C _(l+1,2m+1) −C _(l+1,2m)  (Equation O18)

L _(l,m) =C _(l+1,2m)+(H _(l,m)>>1)  (Equation O19)

C _(l+1,2m) =L _(l,m)−(H _(l,m)>>1)  (Equation O20)

C _(l+1,2m+1) =H _(l,m) +C _(l+1,2m)  (Equation O21)

A recursive integer-to-integer transform will be described. FIG. 126 isa diagram for illustrating a hierarchical transform processing. When theHaar transform is applied to an image, a pair of pieces of data isrequired in order to perform a transform suitable for pixel transform.In the Haar transform for a three-dimensional point cloud, an integerHaar is applied when a three-dimensional point pair, which is a pair ofpoint clouds, can be formed, and data on three-dimensional points ismoved to the subsequent layer (level) when a three-dimensional pointpair is not available. Then, this process is recursively performed.

Next, a configuration of a three-dimensional data encoding device willbe described. FIG. 127 is a block diagram of three-dimensional dataencoding device 8310. Three-dimensional data encoding device 8310generates encoded data (encoded stream) by encoding point cloud data(point cloud). Three-dimensional data encoding device 8310 includesgeometry information encoder 8311, lossless attribute informationencoder 8312, additional information encoder 8313, and multiplexer 8314.

Geometry information encoder 8311 generates encoded geometry informationby encoding geometry information. For example, geometry informationencoder 8311 encodes geometry information using an N-ary tree structure,such as an octree. Specifically, in the case of an octree, a currentspace is divided into eight nodes (subspaces), and 8-bit information(occupancy code) that indicates whether each node includes a point cloudor not is generated. A node including a point cloud is further dividedinto eight nodes, and 8-bit information that indicates whether each ofthe eight nodes includes a point cloud or not is generated. This processis repeated until a predetermined layer is reached or the number of thepoint clouds included in each node becomes smaller than or equal to athreshold.

Lossless attribute information encoder 8312 generates encoded attributeinformation, which is encoded data, by encoding attribute informationusing configuration information generated by geometry informationencoder 8311.

Additional information encoder 8313 generates encoded additionalinformation by encoding additional information included in point clouddata.

Multiplexer 8314 generates encoded data (encoded stream) by multiplexingthe encoded geometry information, the encoded attribute information, andthe encoded additional information, and transmits the generated encodeddata. The encoded additional information is used in the decoding.

FIG. 128 is a block diagram of lossless attribute information encoder8312. Lossless attribute information encoder 8312 includes integertransformer 8321 and entropy encoder 8322. Integer transformer 8321generates a coefficient value by performing an integer transform (suchas an integer Haar transform) on attribute information. Entropy encoder8322 generates encoded attribute information by entropy-encoding thecoefficient value.

FIG. 129 is a block diagram of integer transformer 8321. Integertransformer 8321 includes re-ordering unit 8323 and integer Haartransformer 8324. Re-ordering unit 8323 re-orders attribute informationbased on geometry information. For example, re-ordering unit 8323re-orders attribute information in Morton order. Integer Haartransformer 8324 generates a coefficient value by performing an integerHaar transform on the re-ordered attribute information.

Next, a configuration of a three-dimensional data decoding deviceaccording to this embodiment will be described. FIG. 130 is a blockdiagram showing a configuration of three-dimensional data decodingdevice 8330. Three-dimensional data decoding device 8330 reproducespoint cloud data by decoding encoded data (encoded stream) generated byencoding the point cloud data. Three-dimensional data decoding device8330 includes demultiplexer 8331, a plurality of geometry informationdecoders 8332, a plurality of lossless attribute information decoders8333, and additional information decoder 8334.

Demultiplexer 8331 generates encoded geometry information, encodedattribute information, and encoded additional information bydemultiplexing encoded data (encoded stream).

Geometry information decoder 8332 generates geometry information bydecoding encoded geometry information. Lossless attribute informationdecoder 8333 generates attribute information by decoding encodedattribute information. For example, lossless attribute informationdecoder 8333 generates attribute information by performing an inverseinteger transform (such as an inverse integer Haar transform) on encodedattribute information.

FIG. 131 is a block diagram of lossless attribute information decoder8333. Lossless attribute information decoder 8333 includes entropydecoder 8341 and inverse integer transformer 8342. Entropy decoder 8341generates a coefficient value by entropy-decoding encoded attributeinformation. Inverse integer transformer 8342 generates attributeinformation by performing an inverse integer transform (such as aninverse integer Haar transform) on the coefficient value.

FIG. 132 is a block diagram of inverse integer transformer 8342. Inverseinteger transformer 8342 includes re-ordering unit 8343 and inverseinteger Haar transformer 8344. Re-ordering unit 8343 re-orderscoefficient values based on geometry information. For example,re-ordering unit 8343 re-orders coefficient values in Morton order.Inverse integer Haar transformer 8344 generates attribute information byperforming an inverse integer Haar transform on the re-orderedcoefficient values.

The three-dimensional data encoding device may add, to the header of thebitstream or the like, information that indicates which of thereversible (Lossless) encoding and the irreversible (lossy) encoding hasbeen used. For example, the three-dimensional data encoding device mayadd lossless_enable_flag to the header. When lossless_enable_flag=1, thethree-dimensional data decoding device decodes the reversibly encodedbitstream by applying the inverse integer Haar transform. Whenlossless_enable_flag=0, the three-dimensional data decoding devicedecodes the irreversibly encoded bitstream by applying the inverse RAHT.In this way, the three-dimensional data decoding device can properlydecode the bitstream by changing the inverse transform processing inaccordance with the value of lossless_enable_flag.

Note that the information that indicates which of the reversibleencoding and the irreversible encoding has been used for encoding is notnecessarily limited thereto, and the value of quantization parameter QPor quantization step Qstep can also be used, for example. For example,when the value of the quantization parameter or quantization step is aparticular value (QP=4 or Qstep=1, for example), the three-dimensionaldata decoding device may determine that the bitstream has been encodedby reversible encoding, and decode the reversibly encoded bitstream byapplying the inverse integer Haar transform. When the value of thequantization parameter or quantization step is greater than theparticular value (QP=4 or Qstep=1, for example), the three-dimensionaldata decoding device may determine that the bitstream has been encodedby irreversible encoding, and decode the irreversibly encoded bitstreamby applying the inverse RAHT.

Next, a lossless attribute information encoding processing will bedescribed. FIG. 133 is a flowchart of a lossless attribute informationencoding processing.

First, the three-dimensional data encoding device re-orders attributeinformation on a three-dimensional point cloud (S8301). For example, thethree-dimensional data encoding device re-orders attribute informationon a three-dimensional point cloud in Morton order.

The three-dimensional data encoding device then selects a current pointto be processed from the three-dimensional point cloud (S8302).Specifically, the three-dimensional data encoding device selects theleading three-dimensional point in the three-dimensional point cloudre-ordered in Morton order.

The three-dimensional data encoding device then determines whether ornot there is a three-dimensional point pair (Point Pair), which athree-dimensional point located next to the current three-dimensionalpoint in Morton order (S8303). When there is a three-dimensional pointpair (if Yes in S8304), the three-dimensional data encoding devicegenerates a coefficient value including a high-frequency component and alow-frequency component by performing the integer Haar transform usingthe three-dimensional point pair (S8305). The three-dimensional dataencoding device then encodes (entropy-encodes, for example) thegenerated high-frequency component, and stores the encodedhigh-frequency component in the bitstream (S8306). The three-dimensionaldata encoding device also stores the low-frequency component in a memoryor the like for the processing for the subsequent layer (S8307).

On the other hand, when there is no three-dimensional point pair if Noin S8304), the three-dimensional data encoding device stores theattribute information on the current three-dimensional point in thememory or the like for the subsequent layer (S8307).

When the current three-dimensional point is not the lastthree-dimensional point in the current layer, which is the layer to beprocessed (if No in S8308), the three-dimensional data encoding deviceselects the subsequent three-dimensional point in Morton order as acurrent three-dimensional point (S8302), and performs step S8303 and thefollowing process on the selected current three-dimensional point. The“subsequent three-dimensional point in Morton order” means thethree-dimensional point subsequent to the three-dimensional point pairwhen there is a three-dimensional point pair, and refers to thethree-dimensional point subsequent to the current three-dimensionalpoint when there is no three-dimensional point pair.

When the current three-dimensional point is the last three-dimensionalpoint in the current layer of Yes in S8308), the three-dimensional dataencoding device starts the processing for the subsequent layer (thelayer directly above the current layer) (S8309). When the former currentlayer is not the last layer (top layer) (if No in S8310), thethree-dimensional data encoding device selects the firstthree-dimensional point in Morton order in the subsequent layer as acurrent three-dimensional point (S8302), and performs step S8303 and thefollowing process on the selected current three-dimensional point.

When the former current layer is the last layer of Yes in S8310), thethree-dimensional data encoding device encodes (entropy-encodes, forexample) the low-frequency component generated in the last layer (toplayer), and stores the encoded low-frequency component in the bitstream(S8311). By the process described above, encoded attribute informationis generated which includes the encoded high-frequency component for thethree-dimensional point pair included in each layer and the encodedlow-frequency component for the top layer.

Next, a lossless attribute information decoding processing will bedescribed. FIG. 134 is a flowchart of a lossless attribute informationdecoding processing.

First, the three-dimensional data decoding device decodes coefficientvalues from the bitstream (S8321). The coefficient value includes thehigh-frequency component for the three-dimensional point pair includedin each layer and the low-frequency component for the top layer. Thethree-dimensional data decoding device then re-orders the obtainedcoefficient values (S8322). For example, the three-dimensional datadecoding device re-orders a plurality of high-frequency components inMorton order.

The three-dimensional data decoding device then obtains a low-frequencycomponent to be processed and a high-frequency component to beprocessed, which are the low-frequency component and the high-frequencycomponent of the three-dimensional point pair to be processed (S8323 andS8324). Specifically, the low-frequency component to be processed forthe top layer is the low-frequency component decoded from the bitstream,and the low-frequency component to be processed for the layers otherthan the top layer is the low-frequency component obtained by theinverse transform processing in the layer directly above the layer. Thehigh-frequency component to be processed for the top layer is theleading high-frequency component of the high-frequency componentsre-ordered in Morton order. Note that, when there is nothree-dimensional point pair, there is no high-frequency component to beprocessed.

When there is a three-dimensional point pair (if Yes in S8325), that is,when there is a high-frequency component to be processed, thethree-dimensional data decoding device generates a low-frequencycomponent for the layer directly below the layer by performing theinverse integer Haar transform using the low-frequency component to beprocessed and the high-frequency component to be processed (S8326). Notethat, when the current layer is the bottom layer, attribute informationis generated by the inverse integer Haar transform.

The three-dimensional data decoding device stores the generatedlow-frequency component in a memory or the like for the processing forthe subsequent layer (S8327).

On the other hand, when there is no three-dimensional point pair if Noin S8325), the three-dimensional data decoding device stores thelow-frequency component to be processed in the memory or the like forthe subsequent layer (S8327).

When the coefficient value (three-dimensional point pair) to beprocessed is not the last coefficient value in the current layer (if Noin S8328), the three-dimensional data decoding device selects thesubsequent three-dimensional point pair in Morton order as athree-dimensional point to be processed, and performs step S8323 and thefollowing process on the selected three-dimensional point pair.

When the coefficient value to be processed is the last coefficient valuein the current layer (if Yes in S8328), the three-dimensional datadecoding device starts the processing for the subsequent layer (thelayer directly below the layer) (S8329). When the former current layeris not the last layer (bottom layer) (if No in S8330), thethree-dimensional data decoding device selects the firstthree-dimensional point pair in Morton order in the subsequent layer asa three-dimensional point pair to be processed, and performs step S8323and the following process on the selected three-dimensional point pair.

When the former current layer is the last layer of Yes in S8330), thethree-dimensional data decoding device ends the processing. By theprocessing described above, the attribute information on all thethree-dimensional points is obtained.

Next, example configurations of integer Haar transformer 8324 andinverse integer Haar transformer 8344 will be described. FIG. 135 is adiagram showing an example configuration of integer Haar transformer8324. As shown in FIG. 135, integer Haar transformer 8324 includessubtractor 8351, right shifter 8352, and adder 8353. Here, C₁ and C₂represent attribute information on a three-dimensional point pair forthe bottom layer, and represent low-frequency components of athree-dimensional point pair obtained in the layer directly below thelayer for a layer other than the bottom layer. H represents ahigh-frequency component of a three-dimensional point pair, and Lrepresents a low-frequency component of a three-dimensional point pair.With the configuration in the drawing, the calculations expressed by(Equation O18) and (Equation O19) are implemented.

FIG. 136 is a diagram showing an example configuration of inverseinteger Haar transformer 8344. As shown in FIG. 136, inverse integerHaar transformer 8344 includes right shifter 8354, subtractor 8355, andadder 8356. With the configuration in the drawing, the calculationsexpressed by (Equation O20) and (Equation O21) are implemented.

Note that, in at least any one of the forward transform and the inversetransform, input data may be divided into a plurality of pieces of dataon a predetermined unit basis, and the resulting divisional data may beprocessed in parallel. In this way, the processing speed can beincreased.

Next, an example where the encoding is switched between the reversibleencoding (integer Haar transform) and the irreversible encoding (RAHT)will be described. FIG. 137 is a diagram showing a configuration of athree-dimensional data encoding device in this case. Thethree-dimensional data encoding device selectively performs thereversible encoding (reversible compression) or the irreversibleencoding (irreversible compression). The three-dimensional data encodingdevice may indicate the encoding mode with a flag or QP.

The three-dimensional data encoding device shown in FIG. 137 includesre-ordering unit 8361, switcher 8362, RAHT unit 8363, quantizer 8364,integer transformer 8365, and entropy encoder 8366.

Re-ordering unit 8361 re-orders attribute information in Morton order,for example, based on geometry information. Switcher 8362 outputs there-ordered attribute information to RAHT unit 8363 or integertransformer 8365. For example, switcher 8362 switches between using theRAHT and using the integer Haar transform based on LOSSLESS_FLAG. Here,LOSSLESS_FLAG is a flag that indicates which of RAHT (irreversibleencoding) and the integer Haar transform (reversible encoding) is to beused. The integer Haar transform (reversible encoding) is used whenLOSSLESS_FLAG is on (a value of 1, for example), and RAHT (irreversibleencoding) is used when LOSSLESS_FLAG is off (a value of 0, for example).Alternatively, the three-dimensional data encoding device may determineto use the reversible encoding when the value of quantization parameterQP is particular value α. Here, value α is a value with which thequantized value or the value of quantization step Qstep calculated fromthe QP value is 1. For example, if Qstep=1 when QP=4, α=4.

The switching between RAHT and the integer Haar transform is notexclusively performed based on LOSSLESS_FLAG or QP value, but can beperformed in any manner. For example, the three-dimensional dataencoding device may add an Enable_Integer_Haar_Transform flag to theheader or the like, and applies the integer Haar transform whenEnable_Integer_Haar_Transform=1, and apply RAHT whenEnable_Integer_Haar_Transform=0.

RAHT unit 8363 generates a coefficient value by applying RAHT to theattribute information. Quantizer 8364 generates a quantized coefficientby quantizing the coefficient value. integer transformer 8365 generatesa coefficient value by applying the integer Haar transform on theattribute information. Entropy encoder 8366 generates encoded attributeinformation by entropy-encoding the quantized value generated byquantizer 8364 or the coefficient value generated by integer transformer8365.

FIG. 138 is a diagram showing a configuration of a three-dimensionaldata decoding device corresponding to the three-dimensional dataencoding device shown in FIG. 137. The three-dimensional data decodingdevice shown in FIG. 138 includes entropy decoder 8371, re-ordering unit8372, switcher 8373, inverse quantizer 8374, inverse RAHT unit 8375, andinverse integer transformer 8376.

Entropy decoder 8371 generates coefficient values (or quantizedcoefficients) by entropy-decoding encoded attribute information.Re-ordering unit 8372 re-orders the coefficient values in Morton order,for example, based on geometry information. Switcher 8373 outputs there-ordered coefficient values to inverse quantizer 8374 or inverseinteger transformer 8376. For example, switcher 8373 switches betweenusing RAHT and using the integer Haar transform based on LOSSLESS_FLAG.Note that the way of switching by switcher 8373 is the same as the wayof switching by switcher 8362 described above. Note that thethree-dimensional data decoding device obtains LOSSLESS_FLAG, the QPvalue, or the Enable_Integer_Haar_Transform flag from the bitstream.

Inverse quantizer 8374 generates a coefficient value byinverse-quantizing the quantized coefficient. Inverse RAHT unit 8375generates attribute information by applying inverse RAHT on thecoefficient value. Inverse integer transformer 8376 generates attributeinformation by applying the inverse integer Haar transform on thecoefficient value.

Note that, although no quantization processing is performed when theinteger Haar transform is applied in the example shown in FIG. 137 andFIG. 138, a quantization processing may be performed when the integerHaar transform is applied. FIG. 139 is a diagram showing a configurationof a three-dimensional data encoding device in that case. FIG. 140 is adiagram showing a configuration of a three-dimensional data decodingdevice in that case.

As shown in FIG. 139, quantizer 8364A generates quantized coefficientsby quantizing the coefficient value generated by RAHT unit 8363 and thecoefficient value generated by integer transformer 8365.

As shown in FIG. 140, inverse quantizer 8374A generates coefficientvalues by inverse-quantizing the quantized coefficients.

FIG. 141 and FIG. 142 are diagrams showing example configurations of thebitstream (encoded attribute information) generated by thethree-dimensional data encoding device. For example, as shown in FIG.141, LOSSLESS_FLAG is stored in the header of the bitstream.Alternatively, as shown in FIG. 142, the QP value is included in theheader of the bitstream. The reversible encoding is applied when the QPvalue is predetermined value α.

Embodiment 12

In this embodiment, an integer RAHT, which is an irreversible transformcloser to the reversible transform than the normal RAHT, will bedescribed. In order to facilitate the implementation of the hardware, afixed point RAHT can be introduced. The fixed point RAHT can beimplemented according to the following (Equation O22) and (EquationO23). Here, 1 represents a low-frequency component, and h represents ahigh-frequency component. c₁ and c₂ represent attribute information on athree-dimensional point pair for the bottom layer, and representlow-frequency components of a three-dimensional point pair obtained inthe layer directly below the layer for a layer other than the bottomlayer. The transform is orthonormal, and (Equation O24) holds.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 17} \right\rbrack & \; \\{\begin{bmatrix}l \\h\end{bmatrix} = {\begin{bmatrix}a & b \\{- b} & a\end{bmatrix}\begin{bmatrix}c_{1} \\c_{2}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O22}} \right) \\{{a = \frac{\sqrt{w_{1}}}{\sqrt{w_{1} + w_{2}}}},{b = \frac{\sqrt{w_{2}}}{\sqrt{w_{1} + w_{2}}}}} & \left( {{Equation}\mspace{14mu}{O23}} \right) \\{{a^{2} + b^{2}} = {{\frac{w_{1}}{w_{1} + w_{2}} + \frac{w_{2}}{w_{1} + w_{2}}} = 1}} & \left( {{Equation}\mspace{14mu}{O24}} \right)\end{matrix}$

Weight w after the update is expressed as w=w₁+w₂ when c₁ and c₂ are athree-dimensional point pair, and is expressed as w=w₁ when c₁ and c₂are nota pair.

The above (Equation O22) is transformed into the following (EquationO25) to (Equation O29).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 18} \right\rbrack & \; \\{\mspace{79mu}{\begin{bmatrix}l \\h\end{bmatrix} = {\begin{bmatrix}\frac{\sqrt{w_{1}}}{\sqrt{w_{1} + w_{2}}} & \frac{\sqrt{w_{2}}}{\sqrt{w_{1} + w_{2}}} \\{- \frac{\sqrt{w_{2}}}{\sqrt{w_{1} + w_{2}}}} & \frac{\sqrt{w_{1}}}{\sqrt{w_{1} + w_{2}}}\end{bmatrix}\begin{bmatrix}c_{1} \\c_{2}\end{bmatrix}}}} & \left( {{Equation}\mspace{14mu}{O25}} \right) \\{\begin{bmatrix}\frac{l}{\sqrt{w_{1} + w_{2}}} \\h\end{bmatrix} = {\begin{bmatrix}\frac{w_{1}}{w_{1} + w_{2}} & \frac{w_{2}}{w_{1} + w_{2}} \\{- \frac{\sqrt{w_{1}}\sqrt{w_{2}}}{\sqrt{w_{1} + w_{2}}}} & \frac{\sqrt{w_{1}}\sqrt{w_{2}}}{\sqrt{w_{1} + w_{2}}}\end{bmatrix}\begin{bmatrix}\frac{c_{1}}{\sqrt{w_{1}}} \\\frac{c_{2}}{\sqrt{w_{2}}}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O26}} \right) \\{\mspace{79mu}{\begin{bmatrix}\frac{l}{\sqrt{w_{1} + w_{2}}} \\\frac{h\sqrt{w_{1} + w_{2}}}{\sqrt{w_{1}w_{2}}}\end{bmatrix} = {\begin{bmatrix}\frac{w_{1}}{w_{1} + w_{2}} & \frac{w_{2}}{w_{1} + w_{2}} \\{- 1} & 1\end{bmatrix}\begin{bmatrix}\frac{c_{1}}{\sqrt{w_{1}}} \\\frac{c_{2}}{\sqrt{w_{2}}}\end{bmatrix}}}} & \left( {{Equation}\mspace{14mu}{O27}} \right) \\{\mspace{79mu}{\begin{bmatrix}l^{\prime} \\h^{\prime}\end{bmatrix} = {\begin{bmatrix}a^{2} & b^{2} \\{- 1} & 1\end{bmatrix}\begin{bmatrix}c_{1}^{\prime} \\c_{2}^{\prime}\end{bmatrix}}}} & \left( {{Equation}\mspace{14mu}{O28}} \right) \\{\mspace{79mu}{{l^{\prime} = \frac{l}{\sqrt{w_{1} + w_{2}}}},{h^{\prime} = \frac{h\sqrt{w_{1} + w_{2}}}{\sqrt{w_{1}w_{2}}}}}} & \left( {{Equation}\mspace{14mu}{O29}} \right)\end{matrix}$

Therefore, the forward transform is expressed by (Equation O30) to(Equation O32).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 19} \right\rbrack & \; \\{\begin{bmatrix}l^{\prime} \\h^{\prime}\end{bmatrix} = {\begin{bmatrix}a^{2} & b^{2} \\{- 1} & 1\end{bmatrix}\begin{bmatrix}c_{1}^{\prime} \\c_{2}^{\prime}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O30}} \right) \\{h^{\prime} = {c_{2}^{\prime} - c_{1}^{\prime}}} & \left( {{Equation}\mspace{14mu}{O31}} \right) \\{l^{\prime} = {{{a^{2}c_{1}^{\prime}} + {b^{2}c_{2}^{\prime}}} = {c_{1}^{\prime} + {b^{2}h^{\prime}}}}} & \left( {{Equation}\mspace{14mu}{O32}} \right)\end{matrix}$

The inverse transform is expressed by (Equation O33) to (Equation O34).

[Math. 20]

c ₁ ′=l′−b ² h′  (Equation O33)=

c ₂ ′=h′+c ₁′  (Equation O34)

An adjusted quantization step (Aqs: Adjusted Quantization Step) isexpressed by (Equation O36) based on (Equation O35). Therefore,(Equation O37) holds. In this way, the integer RAHT can be implementedby fixed point implementation of b².

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 21} \right\rbrack & \; \\{h^{\prime} = \frac{h\sqrt{w_{1} + w_{2}}}{\sqrt{w_{1}w_{2}}}} & \left( {{Equation}\mspace{14mu}{O35}} \right) \\{{Aqs} = \frac{{QS}\sqrt{w_{1} + w_{2}}}{\sqrt{w_{1}w_{2}}}} & \left( {{Equation}\mspace{14mu}{O36}} \right) \\{\frac{h^{\prime}}{Aqs} = \frac{h}{QS}} & \left( {{Equation}\mspace{14mu}{O37}} \right)\end{matrix}$

In the following, a relationship between the integer RAHT and theinteger Haar transform will be described. The integer RAHT and theinteger Haar transform can be implemented by a common processing.Specifically, the integer Haar transform can be implemented by settingthe weights for all the layers in RAHT so that w₁=w₂=1.

That is, the forward transform in the integer RAHT is expressed by(Equation O38) to (Equation O40), and the inverse transform is expressedby (Equation O41) to (Equation O42). In addition, (Equation O43) holds.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 22} \right\rbrack & \; \\{\begin{bmatrix}l^{\prime} \\h^{\prime}\end{bmatrix} = {\begin{bmatrix}a^{2} & b^{2} \\{- 1} & 1\end{bmatrix}\begin{bmatrix}c_{1}^{\prime} \\c_{2}^{\prime}\end{bmatrix}}} & \left( {{Equation}\mspace{14mu}{O38}} \right) \\{h^{\prime} = {c_{2}^{\prime} - c_{1}^{\prime}}} & \left( {{Equation}\mspace{14mu}{O39}} \right) \\{l^{\prime} = {c_{1}^{\prime} + {b^{2}h^{\prime}}}} & \left( {{Equation}\mspace{14mu}{O40}} \right) \\{c_{1}^{\prime} = {l^{\prime} - {b^{2}h^{\prime}}}} & \left( {{Equation}\mspace{14mu}{O41}} \right) \\{c_{2}^{\prime} = {h^{\prime} + c_{1}^{\prime}}} & \left( {{Equation}\mspace{14mu}{O42}} \right) \\{{a^{2} = \frac{w_{1}}{w_{1} + w_{2}}},{b_{2} = \frac{w_{2}}{w_{1} + w_{2}}}} & \left( {{Equation}\mspace{14mu}{O43}} \right)\end{matrix}$

In (Equation O38) to (Equation O43), if the weight is set so thatw₁=w₂=1, the forward transform is expressed by (Equation O44) to(Equation O45), and the inverse transform is expressed by (Equation O46)to (Equation O47). That is, the integer Haar transform is implemented.

[Math. 23]

h′=c ₂ ′−c ₁′  (Equation O44)

l′=c ₁ ′−h′/2  (Equation O45)

c ₁ ′=l′−h′/2  (Equation O40)

c ₂ ′=h′−c ₁′  (Equation O47)

Next, an example will be described in which switching occurs between theirreversible encoding (RAHT), the irreversible encoding (integer RAHT)close to the reversible encoding, and the reversible encoding (integerHaar transform). FIG. 143 is a diagram showing a configuration of athree-dimensional data encoding device in this case. Thethree-dimensional data encoding device selectively performs theirreversible encoding (RAHT), the irreversible encoding (integer RAHT)close to the reversible encoding, or the reversible encoding (integerHaar transform). The switching is performed based on a flag or a QPvalue.

The three-dimensional data encoding device shown in FIG. 143 includesre-ordering unit 8401, integer RAHT/Haar transformer 8402, quantizer8403, and entropy encoder 8404.

Re-ordering unit 8401 re-orders attribute information in Morton order,for example, based on geometry information. Integer RAHT/Haartransformer 8402 generates a coefficient value by transforming theattribute information by selectively using the irreversible encoding(RAHT), the irreversible encoding (integer RAHT) close to the reversibleencoding, or the reversible encoding (integer Haar transform).

Specifically, the three-dimensional data encoding device uses thereversible encoding (integer Haar transform) when the value ofquantization parameter QP is particular value α, andRAHT-HAAR_FLAG=HAAR. Here, value α is a value with which the quantizedvalue or the value of quantization step Qstep calculated from the QPvalue is 1. For example, if Qstep=1 when QP=4, α=4. Alternatively, thevalue of α may be different between RAHT and Haar.

For example, when RAHT-HAAR_FLAG=RAHT, and QP is greater than α, theirreversible encoding (RAHT) is used. When RAHT-HAAR_FLAG=RAHT, andQP=α, the irreversible encoding (integer RAHT) close to the reversibleencoding is used. When RAHT-HAAR_FLAG=HAAR, and QP=α, the reversibleencoding (integer Haar transform) is used. When RAHT-HAAR_FLAG=HAAR, andQP is greater than α, the irreversible encoding (RAHT) may be used.

When RAHT-HAAR FLAG=HAAR, integer RAHT/Haar transformer 8402 performsthe Haar transform by setting w₁=w₂=1.

Quantizer 8403 generates a quantized coefficient by quantizing thecoefficient value using QP. Entropy encoder 8404 generates encodedattribute information by entropy-encoding the quantized coefficient.

FIG. 144 is a diagram showing a configuration of a three-dimensionaldata decoding device corresponding to the three-dimensional dataencoding device shown in FIG. 143. The three-dimensional data decodingdevice shown in FIG. 144 includes entropy decoder 8411, inversequantizer 8412, re-ordering unit 8413, and inverse integer RAHT/Haartransformer 8414.

Entropy decoder 8411 generates quantized coefficients byentropy-decoding encoded attribute information. Inverse quantizer 8412generates coefficient values by inverse-quantizing the quantizedcoefficients using QP. Re-ordering unit 8413 re-orders the coefficientvalues in Morton order, for example, based on geometry information.

Inverse integer RAHT/Haar transformer 8414 generates attributeinformation by inverse-transforming the coefficient values byselectively using the irreversible encoding (RAHT), the irreversibleencoding (integer RAHT) close to the reversible encoding, or thereversible encoding (integer Haar transform). Note that the way ofswitching is the same as the way of switching by integer RAHT/Haartransformer 8402 described above. Note that the three-dimensional datadecoding device obtains LOSSLESS_FLAG and the QP value from thebitstream.

FIG. 145 is a diagram showing an example configuration of the bitstream(encoded attribute information) generated by the three-dimensional dataencoding device. For example, as shown in FIG. 145, RAHT-HAAR_FLAG andthe QP value are stored in the header of the bitstream. RAHT-HAAR_FLAGis a flag that indicates which of the irreversible encoding (RAHT), theirreversible encoding (integer RAHT) close to the reversible encoding,and the reversible encoding (integer Haar transform) is to be used. Notethat RAHT-HAAR_FLAG may indicate which of the reversible encoding(integer Haar transform) and the irreversible encoding (RAHT) or theirreversible encoding (integer RAHT) close to the reversible encoding isto be used.

In the following, an example implementation of a configuration forperforming the integer RAHT will be described. The integer RAHT can beimplemented as described below. B represents the integer accuracy of b²,and is expressed by (Equation O48).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 24} \right\rbrack & \; \\{B = \frac{\left( {w_{2} ⪡ {kBit}} \right)}{w_{1} + w_{2}}} & \left( {{Equation}\mspace{14mu}{O48}} \right)\end{matrix}$

kBit represents the accuracy of B. For example, in the case of 8-bitaccuracy, kBit=8. kHalf=(1<<(kBit−1)) represents the accuracy thatsupports a rounding (such as rounding down or rounding off). Theadjusted quantization step (Aqs) can be implemented by (Equation O49).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 25} \right\rbrack & \; \\{{Aqs} = {{sqrt\_ integer}\left( \frac{{QS}*{QS}*\left( {w_{1} + w_{2}} \right)}{w_{1}*w_{2}} \right)}} & \left( {{Equation}\mspace{14mu}{O49}} \right)\end{matrix}$

Here, QS represents a quantization step (Quantization Step). The forwardtransform is expressed by (Equation O50) to (Equation O51).

[Math. 26]

h′=C ₂ ′−c ₁′  (Equation O50

l′=c ₁′+((B*h′+kHalf)>>kBit)  (Equation O31)

The quantized high-frequency component is expressed by (Equation O52).The inverse quantization of the high-frequency component is expressed by(Equation O53).

[Math. 27]

quantized_h′=((Aqs>>1)+(h′<<kBit))/Aqs  (Equation O52)

h′=((quantized_h′*Aqs)+kHalf)>>kBit  (Equation O53)

The inverse transform is expressed by (Equation O54) to (Equation O55).

[Math. 28]

c ₁ ′=l′−((B*h′+kHalf)>>kBit)  (Equation O54)

c ₂ ′=h′+c ₁′  (Equation O55)

In the following, an example will be described in which the integer Haaris implemented by RAHT by using a conditional flag, setting the bitaccuracy of the rounding to be 0, and setting Aqs. When the integer Haaris applied, the three-dimensional data encoding device sets the weightto be 1 (w₁=w2=1). The three-dimensional data encoding device also setskHalf to be 0 (kHalf=0). The three-dimensional data encoding device alsoswitches Aqs as follows. The three-dimensional data encoding device setsAqs=QS when using the integer Haar transform. The three-dimensional dataencoding device sets Aqs=sqrt_integer (((QS*QS)*(w₁+w₂))/(w₁*w₂)) whenusing the integer RAHT. Here, sqrt_integer(n) represents the integerpart of the square root of n. Therefore, (Equation O56) holds.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 29} \right\rbrack & \; \\{B = {\frac{\left( {1 ⪡ {kBit}} \right)}{2} = \left( {1 ⪡ \left( {{kBit} - 1} \right)} \right)}} & \left( {{Equation}\mspace{14mu}{O56}} \right)\end{matrix}$

The forward transform of the integer Haar in RAHT is expressed by(Equation O57) to (Equation O59).

[Math. 30]

h′=c ₂ ′−c ₁′  (Equation O57)

l′=c ₁′((B*h′+0)>>kBit)=c ₁′+(h′>>1)  (Equation O58)

quantized_h′=((Aqs>>1)+(h′<<kBit))/Aqs  (Equation O59)

When the reversible encoding is used, QS is set to be 1, and therefore,(Equation O60) holds. When the reversible encoding is used, thequantization and the inverse quantization may be skipped.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 31} \right\rbrack & \; \\{{quantized\_ h}^{\prime} = {\frac{\left( {1 ⪢ 1} \right) + \left( {h^{\prime} ⪡ {kBit}} \right)}{1} = {h^{\prime} ⪡ {kBit}}}} & \left( {{Equation}\mspace{14mu}{O60}} \right)\end{matrix}$

The inverse quantization of the high-frequency component is expressed by(Equation O61). The inverse integer transform is expressed by (EquationO62) to (Equation O63).

[Math. 32]

h′=(quantized_h′*Aqs)>>kBit  (Equation O61)

c ₁ ′=l′−((B*h′+0)>>kBit)=l′−(h′>>1)  (Equation O62)

c ₂ ′=h′+c ₁′  (Equation O63)

As another example of the implementation of the reversible encoding, thebits shift calculations described below may be used. These calculationsare performed for the integer data type (fixed point calculation). Theattribute information is shifted up with the kBit accuracy before thetransform processing.

When applying the integer Haar, the three-dimensional data encodingdevice sets the weight to be 1 (w₁=w2=1). The three-dimensional dataencoding device also sets kHalf to be 0 (kHalf=0). The three-dimensionaldata encoding device also switches Aqs as follows. The three-dimensionaldata encoding device sets Aqs=QS when using the integer Haar transform.The three-dimensional data encoding device sets Aqs=sqrt_integer(((QS*QS)*(w₁+w₂))/(w₁*w₂)) when using the integer RAHT. Therefore,(Equation O64) holds.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 33} \right\rbrack & \; \\{B = {\frac{\left( {1 ⪡ {kBit}} \right)}{2} = \left( {1 ⪡ \left( {{kBit} - 1} \right)} \right)}} & \left( {{Equation}\mspace{14mu}{O64}} \right)\end{matrix}$

As shown by (Equation O65), the attribute information is shifted up withthe kBit accuracy before the transform processing.

[Math. 34]

c _(i) =c _(i) <<kBit  (Equation O65)

The forward transform of the integer Haar in RATH is expressed by(Equation O66) to (Equation O67), and is performed with the kBitaccuracy.

[Math. 35]

h′=c ₂ ′−c ₁′  (Equation O66)

t_l′=((B*h′+kHalf)>>kBit)  (Equation O67)

In order to remove the floating-point accuracy of B for the floorfunction, the kBit accuracy for the low-frequency component is removed.Therefore, the low-frequency component is expressed by (Equation O68).

[Math. 36]

l′+c ₁′((t_l′>>kBit)<<kBit)  (Equation O68)

The inverse integer transform is expressed by (Equation O69) to(Equation O71). In this way, switching from the integer RAHT can bereduced.

[Math. 37]

t_l′=((B*h′+kHalf)>>kBit)  (Equation O69)=

c ₁ ′=l′−((t_l′>>kBit)<<kBit)  (Equation O70)=

c ₂ ′=h′+c ₁′  (Equation O70)

In the following, an example configuration for the forward transformwill be described. FIG. 146 is a diagram showing an exampleconfiguration of integer RAHT/Haar transformer 8402. Integer RAHT/Haartransformer 8402 includes left shifters 8421, 8422, and 8430, subtractor8423, divider 8424, right shifters 8425, 8427, and 8329, multiplier8426, switcher 8428, and adder 8431.

Left shifters 8421 and 8422 shift up (shift to the left) c1 and c2 bykBit when c1 and c2 are original signals (signals located in the bottomlayer of RAHT) of attribute information. As a result, the bit accuracyof the original signals increases, and therefore, the calculationaccuracy of the transform processing can be improved. Therefore, theencoding efficiency can be improved. When c1 and c2 are signals in alayer higher than the bottom layer of RAHT, the kBit shift-up need notbe applied.

Note that, when the integer Haar transform is applied, and QS=1 (whichmeans the reversible encoding), the three-dimensional data encodingdevice need not apply the kBit shift-up to the original signals of theattribute information located in the bottom layer of RAHT. In this way,the reversible encoding can be achieved while reducing the processingamount.

Subtractor 8423 subtracts shifted-up el from shifted-up c2. Divider 8424divides the value obtained by subtractor 8423 by Aqs. Here, Aqs isexpressed by (Equation O72). integer square root(n) represents theinteger part of the square root of n. That is, Aqs depends on QS(quantization step) and the weight. When the integer Haar transform isused, Aqs is set so that Aqs=QS.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 38} \right\rbrack & \; \\{{Aqs} = {{integer\_ square}{\_ root}\left( \frac{{QS}*{QS}*\left( {w_{1} + w_{2}} \right)}{w_{1}*w_{2}} \right)}} & \left( {{Equation}\mspace{14mu}{O72}} \right)\end{matrix}$

Right shifter 8425 generates high-frequency component h by shifting downthe value obtained by divider 8424. Multiplier 8426 multiplies the valueobtained by subtractor 8423 by B. B is expressed by (Equation O73). Whenthe integer Haar transformer is used, the weight is set so thatw_(i)=w₂=1.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 39} \right\rbrack & \; \\{B = \frac{\left( {w_{2} ⪡ {kBit}} \right)}{w_{1} + w_{2}}} & \left( {{Equation}\mspace{14mu}{O73}} \right)\end{matrix}$

Right shifter 8427 shifts down the value obtained by multiplier 8426.Switcher 8428 outputs the value obtained by right shifter 8427 to rightshifter 8429 when the integer Haar transformer is used, and outputs thevalue obtained by right shifter 8427 to adder 8431 when the integer Haartransformer is not used.

When the integer Haar transformer is applied, right shifter 8429 shiftsdown the value obtained by right shifter 8427 by kBit, and left shifter8430 shifts up the value obtained by right shifter 8427 by kBit. In thisway, the values of lower-order kBit bits are set to be 0. That is, thedigits after the decimal point resulting from the division by a value of2 when the integer Haar transformer is applied can be deleted, and thus,the processing of rounding a value down (floor processing) can beachieved. Note that any method that can achieve the process of roundinga value down can be applied.

Note that, when QS>1 (which means the irreversible encoding) in theinteger Haar transformer, the kBit shift down and the kBit shift up neednot be applied to the value obtained by right shifter 8427. In this way,the accuracy after the decimal point resulting from the division by avalue of 2 can be maintained when the integer Haar transformer isapplied. Therefore, the calculation accuracy and the encoding efficiencyare improved.

When the original signals (located in the bottom layer of RAHT) of theattribute information are not shifted up by kBit, the kBit shift downand the kBit shift up need not be applied to the value obtained by rightshifter 8427. In this way, the processing amount can be reduced.

Adder 8431 generates low-frequency component 1 by adding the valueobtained by left shifter 8430 or right shifter 8427 to the valueobtained by left shifter 8421. Note that, in the calculation for the toplayer, resulting low-frequency component 1 is subjected to the kBitshift down.

Next, an example configuration for the inverse transform will bedescribed. FIG. 147 is a diagram showing an example configuration ofinverse integer RAHT/Haar transformer 8414. Inverse integer RAHT/Haartransformer 8414 includes left shifters 8441 and 8447, multipliers 8442,8443, and 8449, right shifters 8444, 8446, 8450, and 8452, switcher8445, and subtractors 8448 and 8451.

Left shifter 8441 shifts up (shifts to the left) high-frequencycomponent h by kBit. Multiplier 8442 multiplies the value obtained byleft shifter 8441 by Aqs. Note that Aqs is the same as Aqs describedabove with reference to FIG. 146. Multiplier 8443 multiplies the valueobtained by multiplier 8442 by B. Note that B is the same as B describedabove with reference to FIG. 146.

Right shifter 8444 shifts down the value obtained by multiplier 8443.Switcher 8445 outputs the value obtained by right shifter 8444 to rightshifter 8446 when the integer Haar transformer is used, and outputs thevalue obtained by right shifter 8444 to subtractor 8448 when the integerHaar transformer is not used.

When the integer Haar transformer is applied, right shifter 8446 shiftsdown the value obtained by right shifter 8444 by kBit, and left shifter8447 shifts up the value obtained by right shifter 8444 by kBit. In thisway, the values of lower-order kBit bits are set to be 0. That is, thedigits after the decimal point resulting from the division by a value of2 when the integer Haar transformer is applied can be deleted, and thus,the processing of rounding a value down (floor processing) can beachieved. Note that any method that can achieve the process of roundinga value down can be applied.

Note that, when QS>1 (which means the irreversible encoding) in theinteger Haar transformer, the kBit shift down and the kBit shift up neednot be applied to the value obtained by right shifter 8444. In this way,the accuracy after the decimal point resulting from the division by avalue of 2 can be maintained when the integer Haar transformer isapplied. Therefore, a bitstream improved in the calculation accuracy andthe encoding efficiency can be properly decoded.

When the decoded signals (located in the bottom layer of RAHT) of theattribute information are not shifted down by kBit, the kBit shift downand the kBit shift up need not be applied to the value obtained by rightshifter 8444. In this way, the processing amount can be reduced.

Subtractor 8448 subtracts the value obtained by left shifter 8447 orright shifter 8444 from low-frequency component 1. Note that, in thecalculation for the top layer, low-frequency component 1 is shifted upby kBit, and subtractor 8448 subtracts the value obtained by leftshifter 8447 or right shifter 8444 from shifted-up low-frequencycomponent 1.

Multiplier 8449 multiplies the value obtained by subtractor 8448 by −1.Right shifter 8450 shifts down the value obtained by multiplier 8449 bykBit. Subtractor 8451 subtracts the value obtained by subtractor 8448from the value obtained by multiplier 8442. Right shifter 8452 shiftsdown the value obtained by subtractor 8451 by kBit. In this way, theoriginal bit accuracy of c1 and c2 is recovered. By this processing, thedecoding result can be obtained with the original bit accuracy whileimproving the calculation accuracy of the transform processing. Thethree-dimensional data decoding device need not apply the kBit shiftdown to any signal in a layer higher than the bottom layer of RAHT.

Note that, when the integer Haar transform is applied, and QS=1 (whichmeans the reversible encoding), the three-dimensional data decodingdevice need not apply the kBit shift-up to the decoded signals of theattribute information located in the bottom layer of RAHT. In this way,a reversibly encoded bitstream can be properly decoded while reducingthe processing amount.

As stated above, the three-dimensional data encoding device according tothe present embodiment performs the process shown by FIG. 148. Thethree-dimensional data encoding device converts pieces of attributeinformation of three-dimensional points included in point cloud datainto coefficient values (S8401), and encodes the coefficient values togenerate a bitstream (S8402). In the converting (S8401), thethree-dimensional data encoding device performs weighting calculationhierarchically to generate the coefficient values belonging to one oflayers, the weighting calculation separating each of the pieces ofattribute information into a high-frequency component and alow-frequency component. In the weighting calculation, thethree-dimensional data encoding device performs the weightingcalculation using weights fixed or not fixed in the layers. Thebitstream includes first information (at least one of RAHT-HAAR FLAG orQP) indicating whether to fix the weights in the layers.

With this, since the three-dimensional data encoding device is capableof reducing the loss caused by the transform, by fixing the weight for aplurality of layers, the three-dimensional data encoding device iscapable of improving the accuracy.

For example, when the weights are fixed in the layers, the weights arefixed to 1.

For example, as shown by FIG. 146, in the weighting calculation, thethree-dimensional data encoding device subtracts first attributeinformation (e.g., c1) from second attribute information (e.g., c2) tocalculate a first value, the first attribute information and the secondattribute information being included in the pieces of attributeinformation, and divides the first value by a first coefficient (e.g.,Aqs) to calculate the high-frequency component (e.g., h). The firstcoefficient (e.g., Aqs) depends on a quantization step (e.g., QS) andthe weights (e.g., w₁, w₂).

For example, as shown by FIG. 146, in the weighting calculation, thethree-dimensional data encoding device multiplies the first value by asecond coefficient (e.g., B) depending on the weights to calculate asecond value, shifts down the second value by a predetermined bit countand shifts up the second value by the predetermined bit count tocalculate a third value, and adds the third value to the first attributeinformation to calculate the low-frequency component (e.g., 1).

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

The three-dimensional data decoding device according to the presentembodiment performs the process shown by FIG. 149. The three-dimensionaldata decoding device obtains, from a bitstream, first information (atleast one of RAHT-HAAR_FLAG or QP) indicating whether to fix weights inlayers (S8411), decodes coefficient values from the bitstream (S8412),and reverse converts the coefficient values to generate pieces ofattribute information of three-dimensional points included in pointcloud data (S8413). The coefficient values belong to one of the layers.In the reverse converting, the three-dimensional data decoding deviceperforms inverse weighting calculation to generate the pieces ofattribute information, the inverse weighting calculation combining thecoefficient values with a high-frequency component and a low-frequencycomponent. In the inverse weighting calculation, the three-dimensionaldata decoding device performs the inverse weighting calculation usingthe weights fixed or not fixed in the layers, according to the firstinformation.

With this, since the three-dimensional data decoding device is capableof reducing the loss caused by the transform, by fixing the weight for aplurality of layers, the three-dimensional data decoding device iscapable of improving the accuracy.

For example, when the weights are fixed in the layers, the weights arefixed to 1.

For example, as shown by FIG. 147, in the inverse weighting calculation,the three-dimensional data decoding device multiplies the high-frequencycomponent by a first coefficient (e.g., Aqs) to calculate a first value,calculates first attribute information (e.g., c1) included in the piecesof attribute information from a second value based on the low-frequencycomponent (e.g., 1), and subtracts the second value from the first valueto calculate second attribute information (e.g., c2) included in thepieces of attribute information. The first coefficient (e.g., Aqs)depends on a quantization step (e.g., QS) and the weights (e.g., w₁,w₂).

For example, as shown by FIG. 147, in the inverse weighting calculation,the three-dimensional data decoding device multiplies the first value bya second coefficient (e.g., B) depending on the weights to calculate athird value, shifts down the third value by a predetermined bit countand shifts up the third value by the predetermined bit count tocalculate a fourth value, and subtracts the low-frequency component fromthe fourth value to calculate the second value.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

Although only some exemplary embodiments of the present disclosure havebeen described in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data encoding method,comprising: transforming pieces of attribute information ofthree-dimensional points included in point cloud data into coefficientvalues; and encoding the coefficient values to generate a bitstream,wherein in the transforming, weighting calculation is performedhierarchically to generate the coefficient values belonging to one oflayers, the weighting calculation separating each of the pieces ofattribute information into a high-frequency component and alow-frequency component, in the weighting calculation, the weightingcalculation is performed using weights fixed or not fixed in the layers,and the bitstream includes first information indicating whether to fixthe weights in the layers.
 2. The three-dimensional data encoding methodaccording to claim 1, wherein when the weights are fixed in the layers,the weights are fixed to
 1. 3. The three-dimensional data encodingmethod according to claim 1, wherein in the weighting calculation: firstattribute information is subtracted from second attribute information tocalculate a first value, the first attribute information and the secondattribute information being included in the pieces of attributeinformation; and the first value is divided by a first coefficient tocalculate the high-frequency component, and the first coefficientdepends on a quantization step and the weights.
 4. The three-dimensionaldata encoding method according to claim 3, wherein in the weightingcalculation: the first value is multiplied by a second coefficientdepending on the weights to calculate a second value; the second valueis shifted down by a predetermined bit count and shifted up by thepredetermined bit count to calculate a third value; and the third valueis added to the first attribute information to calculate thelow-frequency component.
 5. A three-dimensional data decoding method,comprising: obtaining, from a bitstream, first information indicatingwhether to fix weights in layers; decoding coefficient values from thebitstream; and inverse transforming the coefficient values to generatepieces of attribute information of three-dimensional points included inpoint cloud data, wherein the coefficient values belong to one of thelayers, in the inverse transforming, inverse weighting calculation isperformed to generate the pieces of attribute information, the inverseweighting calculation combining the coefficient values with ahigh-frequency component and a low-frequency component, and in theinverse weighting calculation, the inverse weighting calculation isperformed using the weights fixed or not fixed in the layers, accordingto the first information.
 6. The three-dimensional data decoding methodaccording to claim 5, wherein when the weights are fixed in the layers,the weights are fixed to
 1. 7. The three-dimensional data decodingmethod according to claim 5, wherein in the inverse weightingcalculation: the high-frequency component is multiplied by a firstcoefficient to calculate a first value; first attribute informationincluded in the pieces of attribute information is calculated from asecond value based on the low-frequency component; and the second valueis subtracted from the first value to calculate second attributeinformation included in the pieces of attribute information, and thefirst coefficient depends on a quantization step and the weights.
 8. Thethree-dimensional data decoding method according to claim 7, wherein inthe inverse weighting calculation: the first value is multiplied by asecond coefficient depending on the weights to calculate a third value;the third value is shifted down by a predetermined bit count and shiftedup by the predetermined bit count to calculate a fourth value; and thelow-frequency component is subtracted from the fourth value to calculatethe second value.
 9. A three-dimensional data encoding device,comprising: a processor: and memory, wherein using the memory, theprocessor: transforms pieces of attribute information ofthree-dimensional points included in point cloud data into coefficientvalues; and encodes the coefficient values to generate a bitstream,wherein in the transforming, weighting calculation is performedhierarchically to generate the coefficient values belonging to one oflayers, the weighting calculation separating each of the pieces ofattribute information into a high-frequency component and alow-frequency component, in the weighting calculation, the weightingcalculation is performed using weights fixed or not fixed in the layers,and the bitstream includes first information indicating whether to fixthe weights in the layers.
 10. A three-dimensional data decoding device,comprising: a processor: and memory, wherein using the memory, theprocessor: obtains, from a bitstream, first information indicatingwhether to fix weights in layers; decodes coefficient values from thebitstream; and inverse transforms the coefficient values to generatepieces of attribute information of three-dimensional points included inpoint cloud data, wherein the coefficient values belong to one of thelayers, in the inverse transforming, inverse weighting calculation isperformed to generate the pieces of attribute information, the inverseweighting calculation combining the coefficient values with ahigh-frequency component and a low-frequency component, and in theinverse weighting calculation, the inverse weighting calculation isperformed using the weights fixed or not fixed in the layers, accordingto the first information.